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Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39256455
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10.1038/s41598-024-72343-w
Article
The analysis of art design under improved convolutional neural network based on the Internet of Things technology
Liu Bo liubo15153141697@163.com

Shandong Institute of Petroleum and Chemical Technology, Dongying, 257000 China
10 9 2024
10 9 2024
2024
14 211134 2 2024
5 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
This work aims to explore the application of an improved convolutional neural network (CNN) combined with Internet of Things (IoT) technology in art design education and teaching. The development of IoT technology has created new opportunities for art design education, while deep learning and improved CNN models can provide more accurate and effective tools for image processing and analysis. In order to enhance the effectiveness of art design teaching and students’ creative expression, this work proposes an improved CNN model. In model construction, it increases the number of convolutional layers and neurons, and incorporates the batch normalization layer and dropout layer to enhance feature extraction capabilities and reduce overfitting. Besides, this work creates an experimental environment using IoT technology, capturing art image samples and environmental data using cameras, sensors, and other devices. In the model application phase, image samples undergo preprocessing and are input into the CNN for feature extraction. Sensor data are concatenated with image feature vectors and input into the fully connected layers to comprehensively understand the artwork. Finally, this work trains the model using techniques such as cross-entropy loss functions and L2 regularization and adjusts hyperparameters to optimize model performance. The results indicate that the improved CNN model can effectively acquire art sample data and student creative expression data, providing accurate and timely feedback and guidance for art design education and teaching, with promising applications. This work offers new insights and methods for the development of art design education.

Keywords

Improved convolutional neural network
Internet of Things technology
Art design education
Student creative expression skills
Model training
Subject terms

Computational science
Computer science
Information technology
issue-copyright-statement© Springer Nature Limited 2024
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pmcINTRODUCTION

Research background and motivations

In recent years, the improved convolutional neural network (CNN) has achieved tremendous success in the field of computer vision, leading to significant performance improvements in tasks such as image classification, object detection, and image generation1–3. It has been widely adopted in the field of art design by incorporating deeper network architectures, more complex loss functions, and adaptive optimization algorithms. This has provided more precise and efficient tools for image processing and analysis4–6. Simultaneously, the rapid development of Internet of Things (IoT) technology has brought new opportunities and challenges to the education sector. IoT technology enables seamless connectivity between multiple devices, offering various interactive and communication possibilities in educational settings. In art design education and teaching, improvements in students’ creative expression abilities, image compression quality, and student satisfaction are crucial for enhancing teaching effectiveness and nurturing students’ art design capabilities7–10.

Accurate image processing and analysis are particularly significant in art design education for evaluating the quality of students’ work and guiding their creative process11,12. By combining improved CNN with IoT technology, precision and efficiency in image processing and analysis can be enhanced. This, in turn, provides educators with more precise assessment tools and instructional methods, thereby increasing the scientific rigor and specificity of teaching13–15.

In the contemporary digital landscape, the effectiveness and innovation of art and design education play a pivotal role in nurturing students’ aesthetic concepts and creative expressive abilities. As IoT technology rapidly advances and deep learning finds extensive applications in image processing, a significant opportunity unfolds: how to synergize enhanced CNNs with IoT technology to elevate the quality and efficacy of art and design education. This work aims to address a central question: how can the integration of this cutting-edge technology enhance image processing, student interaction, and creative expression, thereby contributing to the advancement of art and design education?

Research objectives

This work seeks to introduce and implement a pioneering learning framework in art and design education. The approach involves the integration of an enhanced CNN with IoT technology, aiming to deliver precise, real-time feedback and guidance. The primary objectives of the research are to refine the CNN model by augmenting convolutional layers and neurons. Additionally, advanced techniques like batch normalization and dropout layers are incorporated to amplify the accuracy and robustness of image processing. Concurrently, this work leverages IoT technology to establish an experimental environment. This environment can capture images and environmental data throughout the artistic creation process, comprehensively enriching the model’s learning inputs16–18. In order to achieve this goal, this work focuses on two main aspects:

First, based on the advancements in CNN, this work proposes and develops an improved CNN model tailored to the specific needs of art design education. Through optimization of network architecture and enhancements in feature extraction algorithms, the goal is to enhance the accuracy and efficiency of the model in image processing and analysis19,20.

Second, leveraging the interconnected nature of IoT technology, this work explores ways to integrate the improved CNN with IoT technology. Incorporating technologies such as sensors, smart devices, and network connectivity into the CNN model aims to facilitate interaction and information sharing between teachers and students, as well as among students themselves, thereby optimizing the art design teaching experience.

Research innovation and contribution

This work’s innovation is prominently demonstrated through the integration of an enhanced CNN and IoT technology aimed at advancing art and design education. Firstly, the innovation lies in the construction of an improved CNN model. This enhancement strengthens the model’s feature extraction capabilities and effectively addresses overfitting concerns by augmenting the number of convolutional layers and neurons and incorporating batch normalization and dropout layers. This refinement enables the model to capture artwork features more accurately and comprehensively, establishing a dependable foundation for subsequent image processing and analysis. Secondly, the work pioneers the combination of IoT technology with art and design education, establishing an experimental environment incorporating cameras, sensors, and other devices to capture artistic images and environmental data. This comprehensive data input approach enriches the model’s information, facilitating a more profound understanding of artworks in the fully connected layers. Beyond image features, it considers the influence of environmental factors, enhancing comprehension of artistic creation background and intent. This approach provides enhanced teaching support for art and design education. From a technical perspective, the work applies the improved CNN model to art and design education and teaching. During the model application phase, image samples undergo preprocessing and are input into CNN for feature extraction. Simultaneously, sensor data is concatenated with the image feature vector and input into the fully connected layers for a holistic understanding of the artworks. During the model training phase, techniques such as cross-entropy loss function and L2 regularization are employed, optimizing performance through hyperparameter adjustments. This approach enhances the model’s robustness in the learning process, adapting more effectively to new artistic samples, thus improving the practicality and reliability of the model. In summary, the innovation of this work lies in the synergistic application of an enhanced CNN model and IoT technology, elevating the accuracy of artistic sample data acquisition and providing targeted guidance for students’ creative expressions. The technological application enriches the model’s comprehension of artworks, introducing fresh perspectives and methodologies to art and design education and teaching.

This work makes significant contributions to the realm of art and design education across multiple dimensions. Firstly, the integration of an enhanced CNN with IoT technology establishes an innovative learning framework, furnishing more precise and real-time support for art and design education. Secondly, the work’s emphasis on data collection and transmission within the experimental environment allows for the comprehensive consideration of specific environmental factors during the artistic creation process. This approach enhances the model’s specificity and relevance. Most notably, the work is committed to validating the effectiveness of this integrated technology within educational scenarios, offering more comprehensive and personalized guidance throughout the teaching process. This dedication propels forward the landscape of art and design education. These contributions play a pivotal role in expanding the research scope of art and design education, promising students a richer learning experience, and fostering more comprehensive artistic and design capabilities.

Research significance

This work aims to explore the application of enhanced CNNs combined with IoT technology in art design education. The core issue is how to leverage the integration of deep learning and IoT to improve teaching effectiveness and student creative expression in art design. While enhanced CNN models and integrated sensor data have been extensively studied in the past, this work presents new contributions in several ways. First, by introducing IoT technology, this work achieves an organic integration of environmental data and image features. This not only deepens the model’s understanding of art but also provides a new method for analyzing the creative context and the artist’s intent. Next, employing advanced techniques such as batch normalization and dropout layers has enhanced the model’s accuracy and robustness, allowing it to better handle complex art images. Additionally, this work creates a dynamic experimental setting using IoT to collect and analyze environmental data during the art creation process in real time, offering valuable insights and methodological references for future research. Although emerging models like vision transformers demonstrate superior image processing performance, considering the specific needs of this work—such as multimodal data processing and real-time feedback—the use of the enhanced traditional CNN remains highly practical and advantageous. Furthermore, many educational institutions may face limitations in hardware resources, making the improved CNN more compatible with existing systems. These innovations provide new perspectives and methods for enhancing the quality of art design education.

Literature review

In the modern digital age, IoT technology is increasingly integrated into various domains, and art design education is no exception. To better address the challenges and opportunities presented by the IoT environment, researchers have explored the application of IoT technology in art design education using methods such as deep learning, image compression, multi-modal representation learning, and energy auditing. Here are some notable studies in this field: Ullah et al.21 used deep learning methods to detect network security threats in IoT. They employed a system based on deep CNN (DCNN) and evaluated its performance in large-scale experiments. The results demonstrated high accuracy and robustness in detecting various network attacks and anomalies. Qiu et al.22 proposed an enhanced JPEG compression method based on deep residual learning for image compression in IoT. They trained deep residual networks to learn effective compression representations and conducted experimental evaluations on various image datasets. The findings suggested that the method significantly reduced image distortion and improved compression quality compared to traditional image compression. Huang et al.23 introduced a multi-modal representation learning-based IoT recommendation system. They used deep neural networks to learn multi-modal feature representations between users and items and applied these features for recommendations. The method showed significant improvements in recommendation accuracy and user satisfaction. Li et al.24 enhanced network security in IoT through energy auditing. They proposed an energy consumption-based security monitoring method to detect potential security threats by monitoring device energy usage. The method effectively identified network attacks and enhanced the security of IoT systems. Dong et al.25 explored the use of mind-mapping tools to promote sustainable creativity in graphic design education. They provided training and guidance on mind-mapping tools to students, assessed their work, and collected feedback. The results showed that mind-mapping tools helped improve students’ creative expression skills and sustainable design thinking. Murzyn–Kupisz and Hołuj26 focused on balancing fashion design education with sustainability, particularly in relation to the relationship between craftsmanship, art, and business skills. They conducted a comprehensive literature analysis to explore the challenges and opportunities of fashion design education in sustainable development and proposed educational strategies to balance craftsmanship and business skills. McLain27 examined the signature pedagogy in design and technology education. A systematic review of various literature was conducted to define and characterize signature pedagogy in design and technology education and its application and effects in teaching practices were discussed. Boysen et al.28 systematically reviewed relevant literature and summarized the application and effects of game-based learning design in teacher and early childhood education. The review results indicated that game-based learning design contributes to improving motivation, engagement, and knowledge transfer. The review results indicate that engaging learning design is beneficial in improving motivation, participation, and knowledge transfer.

Based on the research findings from the literature reviews, it is evident that the application of IoT technology in art design education holds significant potential for development. The use of deep learning methods for network security threat detection, the enhancement of image compression quality through deep residual learning, and approaches such as multi-modal representation learning and energy auditing have all provided valuable insights and possibilities for advancing art design education. Contemporary scholarly research has extensively delved into the realm of Internet of Things (IoT) technology in art and design education, employing methodologies such as deep learning, image compression, multi-modal representation learning, and energy auditing. These methodologies aim to confront the challenges and seize the opportunities presented by IoT environments. For instance, Ullah et al. leveraged deep learning methods to detect cybersecurity threats in the IoT landscape, while Qiu et al. proposed an image compression method rooted in deep residual learning, enhancing the efficiency of image transmission within IoT networks. These studies form the backdrop for this work, underscoring the noteworthy accomplishments of IoT technology across diverse domains. In contrast to preceding research predominantly centered on network security, image transmission, recommendation systems, etc., this work accentuates the practical application of IoT technology, specifically in the realm of art and design education and teaching. Unlike prior studies concentrating on cybersecurity, image transmission, recommendation systems, etc., this work seamlessly integrates IoT technology with CNN models, deploying them directly in the field of art and design. By capturing art image samples and environmental data in an experimental environment, this work enriches the dataset and transforms theoretical research into tangible tools for art and design education. This diverges from previous studies utilizing mind-mapping tools to stimulate creative thinking or balancing fashion design education with sustainability, showcasing distinctive applied innovation in art and design education within this work. In summary, through the innovative fusion of IoT technology and enhanced CNN models, this work applies them to art and design education and teaching, furnishing more targeted guidance and feedback for students. This integrated application achieves innovation at both the theoretical and practical levels, demonstrating substantial potential in actual educational scenarios. Building upon existing scholarly research, the innovation of this work lies in the comprehensive integration of technology into art and design education, offering novel perspectives and methodologies for the evolution of this field.

Research methodology

Before applying the model, collecting and preparing the data required for art design education and teaching is necessary. This includes image samples of artworks, relevant labeling information, and sensor data, among others29,30. Through IoT technology, environmental characteristics of the artworks, such as light intensity and temperature, can be collected using sensor devices and correlated with the artwork’s images. In the selection of the methodology merging an improved CNN with IoT technology, this work meticulously evaluates several alternative approaches. Considered alternatives encompass traditional image processing methods, singular technology applications, and approaches neglecting environmental factors. Firstly, traditional image processing methods are dismissed due to their potential inadequacy in providing the requisite accuracy and flexibility when dealing with intricate art images, especially those characterized by innovation and complexity inherent in art design. Traditional methods might fall short in capturing the nuances of artworks, whereas this work strives to enhance image processing accuracy for comprehensive support in art and design education. Secondly, the work steers away from the singular technology application approach. Relying solely on CNN or exclusively utilizing IoT technology may fail to harness the synergistic effects of both. These two technologies are inherently complementary, and their integration achieves a higher level of information acquisition and processing, furnishing more comprehensive and accurate support for art and design education. Lastly, the approach neglecting environmental factors is discarded. Omitting IoT technology might result in an oversight of specific environmental factors during art creation, a crucial aspect given that the environment significantly influences the creative process and the artist’s intent. IoT technology facilitates a comprehensive understanding of the artistic background, providing the model with richer and more in-depth input. This work elects to combine the improved CNN with IoT technology for several compelling reasons. Primarily, the aim is to enhance image processing accuracy. The improved CNN model, involving augmentation of convolutional layers and neurons and the introduction of technologies such as batch normalization and dropout layers, facilitates a deeper and more accurate learning of art image features, ultimately improving image processing accuracy. Secondly, the work prioritizes the model’s robustness. The incorporation of technologies like batch normalization and dropout layers aids in standardizing the input data distribution in the network, expediting the training convergence process, and enhancing the model’s stability. This amalgamation of technologies renders the improved CNN model more robust and adaptive, well-suited to different artistic features and student creative expressions. Moreover, by leveraging cameras, sensors, and other devices to capture art image samples and environmental data in an experimental environment, the work provides more dimensional information and takes into account specific environmental factors during art creation, offering the model comprehensive input for a more profound understanding of the art creation background—an essential goal of this work. IoT technology captures environmental data during art creation through sensors and correlates it with art image samples. During the model application phase, the model comprehensively understands the art in the fully connected layer by merging image feature vectors with sensor data. This holistic data input method enhances the model’s understanding of the art creation background and the artist’s intent. Finally, the work strives to offer accurate real-time feedback. The integration of improved CNN and IoT technology enables more precise acquisition of art sample data and student creative expression data, providing accurate and real-time feedback and guidance for art and design education, thereby elevating the quality and effectiveness of education. Given these comprehensive reasons, the integration of improved CNN and IoT technology significantly contributes to the enhancement of quality and effectiveness in art and design education.

During the model application process, the first step is to preprocess the collected artwork images to optimize image quality and reduce noise. Preprocessing steps may involve denoising, brightness adjustment, and standardizing dimensions. Next, CNN is adopted for feature extraction. By stacking multiple convolutional layers and pooling layers, CNN can automatically learn and extract high-level features from the images31–33.

When integrating IoT technology, the data collected from sensor devices can be fused with the image features extracted by CNN to comprehensively understand and analyze the artwork34–36. This can be accomplished by concatenating the sensor data with the feature vectors output by the CNN or by employing other fusion methods. This way, environmental characteristics can be organically combined with the artwork’s features for better analysis and understanding of the artwork37–40. This work employs sophisticated techniques to augment the construction of CNN models, coupled with the application of IoT technology. The model attains a more profound level of learning and feature extraction from art images through an increase in the number of convolutional layers and neurons. The augmentation of convolutional layers and neurons is illustrated in Eq. (1).1 Hout=Hin-Hfilter+2×PpaddingSstride+1

In Eq. (1), Hout represents the height of the output feature map; Hin is the height of the input feature map; Hfilter denotes the height of the convolutional kernel; Ppadding signifies the amount of padding; Sstride is the stride. This design enables the model to effectively capture intricate details in art, enhancing the precision and accuracy of image processing. The inclusion of batch normalization layers contributes to standardizing the distribution of input data in the network, accelerating the training convergence process, and improving the stability of the model. Additionally, the dropout layer effectively mitigates overfitting issues, enhancing the model’s ability to generalize new data. The mathematical formulations for batch normalization and dropout layers are presented in Eqs. (2) and (3), respectively:2 BNx=x-μσ2+ϵ×γ+β

3 Dropoutx,p=x1-p×Bernoulli(1-p)

Here, μ and σ represent the mean and standard deviation of the input; ϵ is a constant to prevent division by zero; γ and β are learnable scaling and shifting parameters. The dropout rate is denoted by p. The amalgamation of these two techniques enhances the robustness and adaptability of the improved CNN model.

This work designs an experimental environment, leveraging cameras, sensors, and other devices, to concurrently capture artistic image samples and environmental data. This experimental configuration not only introduces additional dimensions of information but also takes into account specific environmental factors during artistic creation, providing a more comprehensive input for the model. IoT technology plays a pivotal role in capturing environmental data through sensors during the artistic creation process, associating this data with artistic image samples. The mathematical formulation for the fusion of sensor data is expressed in Eq. (4).4 FusedFeature=Concatenate(Image Feature,Sensor Data)

In Eq. (4), the Concatenate operation merges the image feature vector with sensor data, enabling the model to comprehensively understand the artwork in the fully connected layer. This integrated data input method furnishes the model with richer, multi-layered information, contributing to an enhanced understanding of the artistic context and the present work’s intentions. During the model application phase, the fusion of image feature vectors and sensor data in the fully connected layer elevates the model’s overall comprehension of the artwork. This fusion extends beyond conventional image processing, considering the influence of environmental data and refining the model’s holistic understanding of artistic pieces.

In the model training phase, the cross-entropy loss function served as a pivotal metric to gauge the dissimilarity between the model’s predictions and the actual labels. The mathematical expression for the cross-entropy loss function is articulated in Eq. (5).5 Cross-Entropy Loss=-∑iyilog(y^i)

In Eq. (5), yi signifies the probability distribution of the actual labels, while y^i denotes the model’s predicted probability distribution. This widely employed loss function in deep learning, especially for classification tasks, facilitates the effective training of the model to discern between various categories of artworks. To further refine the training process and prevent overfitting, L2 regularization was introduced, aiding in controlling the model’s complexity. The mathematical formulation for L2 regularization is expressed in Eq. (6).6 L2Regularization=λ2∑jθj2

In Eq. (6), λ signifies the regularization parameter; θj represents the model’s parameters. By penalizing the sum of squared model parameters, L2 regularization imparts robustness to the model, enabling better generalization to unseen data. The work constructs a more potent and adaptive CNN model through the comprehensive application of the aforementioned enhancement techniques. The integration of IoT technology further facilitated the model in obtaining a more comprehensive understanding of artworks. Collectively, this integration furnishes more precise and comprehensive support for art design education and teaching. The orchestrated application of these techniques fortifies the model’s robustness and elevates its adaptability, underscoring its efficacy in the context of art and design education. The structure for IoT-based art design education and teaching model is summarized. Figure 1 illustrates the overall model structure.Fig. 1 The model structure diagram for IoT-based art design education and teaching.

The CNN model, through its multiple layers of convolution and pooling operations, can automatically learn and extract high-level features from artistic images. These features include colors, textures, shapes, and other visual elements, which are key to understanding the artistic style and creative characteristics of artworks. For example, by analyzing images from different artistic genres, the CNN model can identify the distinctive lighting effects in impressionist works, the geometric shape decomposition in cubist art, or the vibrant colors and brushwork in abstract expressionism. Students can gain deeper insights into the characteristics of different artistic styles through the analysis of these features, thereby inspiring their creative thinking. The combination of image features extracted by the CNN model and environmental data collected by IoT technology can provide comprehensive support for art and design education. For instance, environmental data such as light intensity and temperature changes collected during the art creation process using IoT technology can be inputted into the CNN model along with the image features. This fusion of multimodal data helps students and educators to better understand the creative background of artworks, including the artist’s intentions and emotional expressions. For example, by analyzing the impact of changes in lighting conditions in the studio on the colors of the artwork, students can better grasp the lighting effects and emotional atmosphere of the artwork. The analysis results of the CNN model can provide real-time feedback and guidance for art and design education. During the teaching process, teachers can use the CNN model to quickly assess the visual characteristics of students’ works, and promptly identify their strengths and weaknesses in composition, and color application. Based on these analysis results, teachers can provide personalized guidance and suggestions to help students improve their artistic skills and enhance their expressive abilities. Moreover, real-time feedback encourages students to engage in immediate learning and adjustment, promoting their active exploration and innovation. In summary, the improved CNN model combined with IoT technology can enhance the scientific rigor and interactivity of art and design education, while also providing students with a richer, more diverse learning environment to stimulate their creativity and imagination. The application of this technology heralds promising prospects for the field of art and design education in terms of technology integration and innovative teaching methodologies.

The model proposed employs an enhanced CNN architecture combined with IoT technology to perform in-depth feature extraction and analysis of art design works. The overall structure of the model includes several key modules. The input layer handles preprocessed image samples and environmental data collected from sensors, encompassing not only visual elements but also contextual factors such as light intensity and temperature. In the convolutional and pooling layers, multiple convolutional layers extract high-level features from the images, such as color, texture, and shape, which are crucial for understanding artistic style and creation characteristics. The feature fusion layer then combines the extracted image features with sensor data to form a multimodal data input, providing more contextual information to the fully connected layer and allowing for a more comprehensive analysis. The fully connected layer synthesizes these multimodal data to understand the overall background of the art piece and the creator’s intent, while the output layer provides real-time feedback and guidance, including evaluations of student works and personalized teaching suggestions. From a machine learning perspective, the model’s primary tasks are image feature extraction and comprehensive analysis. Although it does not fall into typical categories like image classification, enhancement, or generation, it offers a new perspective for educational purposes through feature extraction and data fusion. Image feature extraction is akin to classification tasks, automatically learning and extracting significant features of art pieces, while comprehensive analysis leverages IoT data to provide an in-depth understanding of the creation context and emotional expression, going beyond simple image processing. This design allows the model not only to identify the visual features of art pieces but also to provide more comprehensive educational support through the integration of environmental data.

The enhanced CNN combined with IoT technology is used for art design education, and the design of the model’s input and output is of significant importance. The input section includes preprocessed digital art samples optimized through steps like image denoising, brightness adjustment, and size normalization to ensure data quality. Additionally, environmental sensor data collected via IoT devices, such as light intensity and temperature changes, provide essential background support for understanding the art pieces. In terms of output, the model does not merely generate a single category label but provides results across multiple dimensions, including detailed reports on high-level visual features of the art pieces, such as color, shape, and texture. Based on the analysis of the works and environmental factors, the model also offers personalized creation guidance and teaching suggestions for students and teachers. Furthermore, in practical teaching environments, the model provides real-time evaluations of student works, enabling interactive feedback and enhancing learning outcomes. This design aims to comprehensively improve the quality and efficiency of art design education.

Experimental design and performance evaluation

Datasets collection

Data collection is a critical step. First, sensors and cameras built using IoT technology are employed to collect samples of art pieces. These devices are installed in art studios or exhibition spaces to capture and record images and information of the artworks41,42. When collecting samples of art and design works, it is ensured that the samples come from different times, scenes, and artists with diverse styles and backgrounds. This work also compares various art pieces to ensure that the dataset covers a wide range of artistic styles, colors, textures, and other attributes. Additionally, during the preprocessing of input data, it employs various data augmentation techniques such as rotation, flipping, and scaling to further enhance the robustness and generalization ability of the model.

Additionally, creative expression data from students in art design education are collected. Leveraging IoT technology can capture real-time images, videos, or audio recordings of students’ creative artworks and their behaviors and feedback during the creative process. The specific processes for data collection and model establishment are analyzed. Figure 2 illustrates the overall structure of the model construction.Fig. 2 Specific structure diagram for data collection and model construction.

This work employs a meticulous data integration process to ensure the effective fusion of image feature vectors with sensor data, thereby providing comprehensive support for art and design education. First, it ensures that the collected environmental data corresponds to the respective art image samples in time. In order to achieve this, each art piece’s image and its corresponding environmental data are assigned a unique identifier. By matching these identifiers, it ensures that each set of environmental data corresponds to the correct art image for subsequent analysis. Since image feature vectors and sensor data may exist in different magnitudes and scales, direct fusion may lead to the dominance of certain features, affecting the training effectiveness of the model. In order to mitigate this issue, all data undergo min–max normalization to scale the data to a range of 0 to 1. This step helps balance the weights between different features, enhancing the model’s generalization ability and stability. For image data, CNN is utilized to automatically learn and extract key features from the images. CNN, through multiple layers of convolution and pooling operations, captures hierarchical information in images such as edges, textures, and shapes. As for structured sensor data, since it already exists in numerical form, it can be directly used as feature input. Finally, the normalized image feature vectors are concatenated with the environmental data. This step is typically performed before the fully connected layers of the CNN, and the concatenated feature vector serves as the input to the fully connected layers. Through this approach, the model not only learns the visual features of art images but also considers environmental factors during the art creation process, thereby providing a more comprehensive understanding and evaluation of art pieces. The output of the fully connected layers serves as the model’s final decision basis for various applications in art and design education, such as student work assessment and artistic style analysis. Through the aforementioned data integration process, leveraging environmental data collected by IoT technology combined with the powerful image processing capabilities of CNNs, a comprehensive and accurate analytical tool is provided for art and design education. This not only enhances the technological content of art and design education but also provides educators and students with richer teaching and learning resources.

Experimental environment

Environmental data collected from IoT devices is fused with image feature vectors extracted by the CNN model to achieve a comprehensive understanding of the art pieces. The specific fusion process is as follows:

First, F=[f1,f2,⋯,fn] is set as the image feature vector extracted by the CNN model, where n represents the dimension of the features. S=[s1,s2,⋯,sm] is set as the environmental data vector collected by the IoT devices, where m represents the number of environmental parameters.

This work uses concatenation to fuse these two vectors, resulting in a comprehensive feature vector V. The mathematical equation for this operation is:7 V=ConcatF,S=[f1,f2,⋯,fn,s1,s2,⋯,sm]

The comprehensive feature vector V is then input into the fully connected layer for further processing and analysis. This approach allows the model to learn the visual features of art images and to consider specific environmental factors from the art creation process, providing a more thorough understanding and evaluation of the artwork.

In order to achieve real-time data collection and transmission, this work establishes a comprehensive IoT environment. Required resources type and quantity: cameras: three high-resolution cameras (such as 1920 × 1080 pixels) for capturing images of artworks. Costs vary depending on brand and quality, roughly between 3000 and 6000 yuan each. Sensors include but are not limited to light sensors and temperature sensors for collecting environmental data during art creation. Costs for each sensor range approximately from 100 to 500 yuan. Sufficient storage space for saving images and sensor data is required, which can be a local server or cloud storage service. The cost of cloud services depends on storage capacity and usage. Network devices including routers and switches to ensure wireless or wired data transmission. Costs vary depending on device performance, generally between 500 and 2000 yuan. Recommendations for different skill levels: for beginners, starting with a simple single-camera system and gradually adding sensors and cameras is advisable. Existing educational resources such as school labs or libraries can be adopted to reduce initial investment. For educators with some experience, exploring the use of open-source software and hardware platforms such as Raspberry Pi with Arduino sensors to build experimental environments is recommended. For advanced users, developing custom sensors or using advanced image capture techniques such as 3D scanners to obtain richer data is suggested. Open source tools and platforms: node-RED is used for programming and integrating different IoT devices, with active community support. Google Cloud or Microsoft Azure provides free storage and computing resources suitable for small projects and educational purposes. Platforms like Coursera and edX offer courses on IoT and machine learning to help educators enhance their professional knowledge. During the construction of the IoT experimental environment, significant initial investment can be reduced by choosing cost-effective devices and utilizing open-source resources. Furthermore, by providing detailed guides and tutorials, even non-professional educators can establish and maintain such experimental environments. In the model training phase, cross-entropy loss function and L2 regularization technique are used, and hyperparameters are adjusted to optimize model performance. Table 1 provides specific details of the experimental environment setup: Table 1 Setting of experimental parameter values for the IoT environment.

Parameter	Setting values	Notes	
Sensitivity threshold of the light sensor	100 lx	Determine the minimum brightness threshold for the light sensor’s response	
Sampling frequency of the temperature sensor	Collect data every 5 s	Determine how often the temperature sensor collects data	
Measurement range of the temperature sensor	 − 10–40 °C	The temperature range that the temperature sensor can measure	
Resolution of the camera for capturing images	1920 × 1080 pixels	Configure the resolution for the camera’s image capture	
The frame rate of the camera	30 frames per second	Specify the number of frames per second that the camera captures	
Data transmission method	Wireless or wired	Determine the communication method for uploading data from the sensors and camera	
Location of data storage	Server or cloud platform	Specify the location for data storage after transmission, which can be either a local server or a cloud platform	

The table above provides specific parameter values and their settings for the IoT experimental environment setup. Data collection and image capture can be tailored to meet specific requirements by configuring the sensitivity threshold of the light sensor, the sampling frequency and measurement range of the temperature sensor, and the camera’s resolution and frame rate43,44.

Parameters setting

This work adopts an improved CNN model specifically tailored to the needs of art and design education. The model, based on DCNN, undergoes optimization of its network architecture to enhance the accuracy and efficiency of feature extraction. Architectural details of the model include increasing the number of convolutional layers, introducing batch normalization layers, incorporating dropout layers, applying L2 regularization, and selecting appropriate loss functions and optimization algorithms. These enhancements aim to improve the model’s ability to process artistic images and provide more comprehensive and accurate support for art and design education. Compared to existing CNN models, this customized model offers several advantages in the field of art and design education. First, by increasing the number of convolutional layers and neurons, the proposed model can more accurately capture the features of artistic images, thus enhancing its feature extraction capability. Second, the introduction of batch normalization and dropout layers enhances the model’s robustness and generalization ability, enabling it to better adapt to different artistic styles and data variations. Additionally, methods such as early stopping and regularization are employed to effectively suppress overfitting. Early stopping strategy is a conventional approach to handle overfitting. This work stops training when the performance on the validation set no longer improves, preventing the model from being overly trained on the training set. Furthermore, techniques like weight decay, and L1/L2 regularization are set to limit the weights, reducing model complexity and the risk of overfitting. Moreover, data augmentation techniques such as rotation, scaling, jittering, and flipping, are applied to existing artistic design sample images to increase the diversity of training data. These techniques effectively improve the model’s generalization ability and further suppress overfitting. Finally, optimized network structures and parameter settings enable the model to process image data more quickly, providing real-time feedback for art and design education, thereby enhancing teaching effectiveness. An automated parameter optimization strategy is introduced in the model design. Bayesian optimization method is adopted, which is a probabilistic model-based global optimization technique that effectively searches for the optimal solution of model parameters. Through this approach, the proposed model can automatically adjust parameters such as the number of convolutional layers, the number of neurons, and regularization coefficients during the training process, finding the optimal parameter configuration. Table 2 provides specific model parameter settings. Table 2 Specific parameter settings for the improved CNN model.

Parameter	Setting values	Notes	
Basic model	DCNN	As the infrastructure for improving the model	
Number of convolutional layers	4	The total number of convolutional layers in the model	
Number of neurons in the convolutional layers	64, 128, 256, 512	The number of neurons in each convolutional layer	
Batch normalization layer ratio	0.5	Scaling parameter in the batch normalization layer	
Dropout layer ratio	0.5	The proportion parameter in the Dropout layer	
L2 regularization coefficient	0.0001	Used to control the regularization intensity of the model	
The optimal number of iterations	100	The optimal number of iterations selected through cross-validation	
Loss function	Cross-entropy loss function	Used to measure the gap between the model output and the actual label	
Optimization algorithm	Stochastic gradient descent	An algorithm used to optimize model parameters	

The table above provides specific parameter settings for the improved CNN model used for real-time monitoring of art sample data and student creative expression data. The performance of the proposed improved educational model of convolution network combined with IoT (IoT-CNN) for education is then compared with the performance of traditional education and teaching model based on reinforcement learning (RLET) and education and teaching model based on multi-modal deep neural network (MDNN). RELT is a reinforcement learning-based model that learns the optimal policy through interaction with the environment. In art and design education, the RELT model can be used to simulate students’ decision-making behaviors during the creative process and guide them towards improving their creative skills through reward mechanisms. However, the RELT model may require a large amount of training data and a long training time to achieve the desired performance, which may be restrictive in practical educational scenarios. MDNN is a deep learning model for handling multimodal data, capable of simultaneously processing different types of data such as images, text, and audio. In art and design education, MDNN can analyze students’ multidimensional creative performance, including visual artworks, design explanations, and verbal expressions. Although MDNN performs well in handling multimodal data, it may not be as effective as the IoT-CNN approach in terms of real-time performance and personalized feedback.

The “safety detection rate” is an important metric for assessing the model’s ability to identify and respond to potential safety threats in the art design education environment. This metric is defined as the ratio of the number of safety threats correctly identified by the model to the total number of safety threats. Its mathematical expression reads:8 Security detection rate=NumberofcorrectlyidentifiedsecuritythreatsTotalnumberofsecuritythreats×100%

In the experiment, various data from the art creation environment are first collected using IoT devices, including but not limited to images, temperature, and humidity. The CNN model is then used to analyze these data to identify potential safety threats, such as abnormal behavior or environmental changes. For each identification cycle, the number of safety threats detected by the model is recorded and compared with the actual number of safety threats that have occurred to calculate the safety detection rate.

Performance evaluation

Figure 3 displays the data variation curves for different models concerning the accuracy of recommendations in art design education. Figure 4 shows the data variation curves for different models regarding the safety detection rate in art design education.Fig. 3 Data variation curves depicting the recommendation accuracy for different models in art design education.

Fig. 4 Data variation curves depicting the safety detection rates for different models in art design education.

Figure 3 illustrates that for the RLET model, the recommendation accuracy starts relatively low in the initial phase (first 100 iterations). However, it gradually improves with an increase in the number of iterations, eventually stabilizing in later iterations. This suggests that the model may require more training data and iterations to achieve higher recommendation accuracy. The MDNN model exhibits relatively high recommendation accuracy in the initial phase (around 100–200 iterations) but experiences a slight decline in later iterations, maintaining a relatively stable level.

Figure 4 reveals that the IoT-CNN model achieves a relatively high safety detection rate of 53.45% after 100 iterations. However, as the number of iterations continues to increase, its safety detection rate starts to decline, reaching 77.87% after 600 iterations. Table 3 presents the real-time performance of various models in art design education. Table 3 Real-time performance comparison of various models in art design education.

Model	Image processing speed (frames/second)	Sensor data processing speed (data points/second)	Response time (milliseconds)	
RLET model	20	1000	50	
MDNN model	18	900	55	
IoT-CNN model	25	1200	40	

Table 3 illustrates the real-time performance of different models in art design education. The IoT-CNN model excels in image processing speed, sensor data processing speed, and response time. Specifically, the IoT-CNN model achieves the highest image processing speed at 25 frames per second, showcasing remarkable swiftness compared to other models. Simultaneously, it demonstrates outstanding capabilities in sensor data processing speed, reaching 1200 data points per second. Ultimately, the IoT-CNN model boasts the shortest response time, clocking in at 40 ms, underscoring its exceptional real-time performance.

Additionally, Fig. 5 displays the data trend for image compression quality scores for different models in art design education. Figure 6 presents the data trend for student satisfaction with different models in art design education. Finally, Fig. 7 illustrates the data trend for student creative expression ability scores with different art design education models. The quality score of artistic design image compression is calculated by considering the compression ratio, image clarity, and preservation of key details. First, the compressed images are visually assessed, including their clarity and recognizability. Then, the compression ratio is calculated to determine the degree of image compression. Finally, a comprehensive score is given after considering these factors together. Student satisfaction score is calculated through feedback surveys and assessments targeting students participating in art and design education activities. Student creative performance score is evaluated by assessing students’ creative abilities and performance levels demonstrated during art and design education activities. The assessment includes aesthetic evaluation of student works, creativity, and technical application, along with students’ presentations and verbal statements, to comprehensively evaluate their creative performance.Fig. 5 The data trend for image compression quality scores for different models in art design education.

Fig. 6 The data trend for student satisfaction with different models in art design education.

Fig. 7 The data trend for student creative expression ability scores with different art design education models.

Figure 5 reveals that the scores for all three models gradually increase as the number of iterations increases. This indicates a significant improvement in the quality of compressing art design images as the models continue to learn and optimize. Specifically, the RLET model’s score increases from an initial value of 2.1 to a final value of 4.0. Although the rate of increase is relatively slow, it exhibits an overall upward trend. The MDNN model’s score increases from an initial value of 4.8 to a final value of 6.5.

Figure 6 illustrates that all three models have made some progress in increasing student satisfaction. The IoT-CNN model, in particular, demonstrates higher performance and stability in art design education, consistently receiving relatively high satisfaction scores. However, further research and practical application are needed to validate the applicability and effectiveness of these models in different teaching scenarios.

Figure 7 suggests that the RLET model has relatively low student creative expression ability scores in the initial phase (first 100 iterations). However, these scores gradually improve with an increase in the number of iterations, eventually stabilizing in later iterations. This suggests that the model can enhance students’ creative expression ability through iterative learning and continuously improve their scores during training. Next, the MDNN model exhibits relatively high student creative expression ability scores in the initial phase (around 100–300 iterations), but they experience a slight decline in later iterations, maintaining a relatively stable level.

Compared to the RELT and MDNN methods, the IoT-CNN method demonstrates advantages in several aspects: by increasing the number of convolutional layers and neurons and introducing batch normalization and dropout layers, IoT-CNN improves the accuracy of image processing and analysis. The IoT-CNN model outperforms RELT and MDNN models in terms of image processing speed, sensor data processing speed, and response time, which is particularly important in educational scenarios requiring real-time feedback. Additionally, the IoT-CNN model performs better in terms of user input response time and user experience ratings, providing a smoother and more satisfactory interactive experience. In summary, the application of the IoT-CNN method in art and design education provides more accurate image analysis and real-time feedback. Moreover, it offers personalized guidance based on students’ creative performance, thereby effectively enhancing teaching quality and students’ creative abilities.

Table 4 presents the interactive performance of different models in art design education. Table 4 Interactive performance comparison of different models in art design education.

Model	User input response time (milliseconds)	User experience score (out of 10)	
RLET model	60	8.5	
MDNN model	55	8.8	
IoT-CNN model	40	9.2	

Table 4 illustrates the interactive performance of various models in art design education. The IoT-CNN model has demonstrated outstanding results, achieving a user input response time of 40 ms faster than the other two models. Furthermore, the IoT-CNN model attains the highest user experience score of 9.2, indicating superior user satisfaction with its interactive performance. This achievement can be attributed to incorporating IoT technology and designing an enhanced CNN model, making the model more responsive and efficient in interactions with users.

Figure 8 illustrates the feature extraction for different art styles. It shows the high-level features extracted by the model when processing works of impressionism, cubism, and abstract expressionism. It is evident that artworks of different styles exhibit significant differences in color, shape, and texture, which are effectively captured by the CNN.Fig. 8 Feature extraction for different art styles.

Figure 9 shows the CNN visualization results of student-created works. The analysis highlights the student’s innovations in color usage and composition, providing teachers with targeted feedback basis.Fig. 9 CNN visualization results for student-created works.

Comparison and analysis with existing educational models

In the field of art and design education, common educational models include traditional lecture-based models, problem-based learning (PBL) models, and technology-based interactive learning models. These models each have their strengths. The traditional lecture-based model emphasizes the systematic delivery of fundamental knowledge, the PBL model focuses on practical skills and problem-solving abilities, and the technology-based model utilizes modern information technology to enhance teaching interactivity and learning efficiency. Compared to the aforementioned models, the IoT-CNN model combines IoT technology and deep learning to provide real-time monitoring of art sample data and students’ creative expressions, offering precise feedback and guidance. The model outperforms traditional models in terms of image processing speed, sensor data processing speed, and response time, demonstrating outstanding real-time performance. Through deep learning algorithms, the IoT-CNN model can provide personalized guidance based on students’ creative characteristics, which is challenging for traditional models to achieve. Table 5 displays the differences in key performance indicators between the IoT-CNN model and traditional lecture-based, PBL, and technology-based interactive models. It can be observed that the IoT-CNN model exhibits relative advantages in multiple key performance indicators, particularly in real-time feedback and learning efficiency. Besides, the traditional lecture-based model shows relatively lower levels of student engagement and creative expression. The PBL model demonstrates high levels of learning outcomes and student engagement, but slightly lacks in real-time feedback. The technology-based interactive model performs well in terms of learning outcomes and student engagement but slightly lags behind the PBL model in creative expression. Table 5 Comparison of IoT-CNN model with traditional lecture model, PBL model, and technology-interactive model on key performance indicators.

Model	Learning efficiency	Student engagement	Creativity	Real-time feedback	
IoT-CNN Model	High	High	High	High	
Traditional lecture model	Medium	Low	Low	Low	
PBL model	High	High	High	Medium	
Technology-interactive model	High	High	Medium	High	

Empirical validation

In order to further validate and optimize the IoT-based CNN model proposed, collaborative agreements have been reached with schools and art education training institutions to apply and test the model in their courses. The model runs in real teaching environments, collecting various types of data generated during the teaching process, including but not limited to samples of art design images, environmental data, and student work. Additionally, feedback is collected from both teachers and students to understand the performance and acceptance of the model in practical applications. Empirical research involves periodic satisfaction surveys with teachers and students using the model to understand their acceptance and the actual effects of the model in teaching. Regular communication is maintained with teachers using the model, meticulously recording their experiences, and any issues or suggestions they may have. The learning outcomes of students are tracked to evaluate the effectiveness of the model in enhancing students’ artistic skills. Table 6 presents the empirical validation results. It reveals that the IoT-CNN model has received positive evaluations in art design education, particularly in enhancing students’ innovativeness and expressiveness. Table 6 Empirical validation results.

Validation content	Result indicators	Average score	Standard deviation	Remarks	
Student satisfaction	Rating from 1 to 10	8.5	0.8	High satisfaction	
Teacher satisfaction	Rating from 1 to 10	7.9	1.1	Satisfaction is relatively high, with room for improvement	
Teaching effectiveness	Innovation and technicality ratings	4.6	0.6	Innovation has improved, and technicality needs strengthening	
Student artistic quality	Artistic understanding and expressiveness ratings	8.2	1.2	Significant improvement in expressiveness	

This work also aims to understand the direct impact of the proposed model on students’ learning outcomes in art and design education. In order to achieve this, a series of field teaching experiments and qualitative surveys are conducted to collect and analyze data on students’ creative performance and engagement. The IoT-CNN model is deployed in a real teaching environment, and comparisons are made with traditional teaching methods. The experimental design is as follows: the experimental subjects are students majoring in art and design, who are divided into two groups. One group uses traditional teaching methods, while the other group uses the IoT-CNN model as an auxiliary teaching tool. The teaching observations last for eight consecutive weeks. Table 7 presents the comparative results of the model in the field teaching environment. Table 7 Comparative results of the model in field teaching environment.

Indicator	Experimental group (IoT-CNN model)	Control group (traditional teaching)	Improvement rate (%)	
Creativity	4.2	3.1	35.5	
Skill application	4.0	3.3	21.2	
Personal style development	4.1	3.2	28.1	

In order to gain deeper insights into students’ perceptions and user experiences with the IoT-CNN model, this work conducts the following survey. The survey participants are students using the IoT-CNN model, and Table 8 summarizes the survey results. The survey findings indicate that the majority of students hold a positive attitude towards the IoT-CNN model, believing that it provides a more flexible and personalized learning approach. Table 8 Survey results summary.

Theme	Description	Student feedback summary	
Flexibility in learning style	Students believe that the IoT-CNN model makes the learning process more flexible and personalized	“I can adjust the learning content according to my own pace, which is very personalized.”	
Depth of understanding of artworks	The model helps students to better understand the creative background and technical details of artworks	“I can better understand the intentions and emotions behind artworks.”	
Enhancement of interest in art and design	Students express that the model has increased their interest and understanding in art and design	“My interest in art and design has become stronger than before.”	
Value of real-time feedback and guidance	Students appreciate the real-time feedback and guidance provided by the model	“Timely feedback allows me to immediately improve my work.”	

In summary, the consistent results from field teaching experiments and qualitative research indicate that the IoT-CNN model effectively promotes students’ creativity and engagement in art and design education. These findings provide strong evidence for the application of the model in art and design education and point the way for future developments in educational technology.

In order to gain deeper insights into user experience, feedback from art and design teachers and students is collected. Based on the data, an analysis is conducted on users’ perceptions during the model’s usage and their system usability scale (SUS) scores. Table 9 presents the results of the user experience survey. The survey results indicate that users are generally satisfied with the model’s interface, believing that it provides timely and accurate feedback on art and design. Particularly in the extraction of artistic features and analysis of environmental factors, users’ feedback suggests that the model demonstrates good performance. However, there are also areas for improvement. Some users mention that it takes them a relatively long time to familiarize themselves with the operation process when using the model for the first time. This indicates a need for enhanced user-friendliness in the design of subsequent developments. Additionally, users also note that there is further room for optimization in capturing complex textures and subtle color variations when dealing with intricate art images. Table 9 Survey results of user experience.

Survey indicators	Description	Average score	Standard deviation	Key points of user feedback	
Ease of use	Intuitiveness of interface and workflow	4.2	0.5	Satisfactory interaction interface, need for improved user-friendliness for novices	
Accuracy	Accuracy of feedback and recommendations	4.5	0.4	Accurate feature extraction, improvement needed in capturing details	
Smoothness of interaction	Smoothness of interaction	4.3	0.6	The operation process could be more concise	
Overall satisfaction	Satisfaction with the model	4.4	0.5	High satisfaction level, desire for more personalized features	
SUS score	System usability	82	7	High overall system usability	

Based on user feedback, the following optimizations are made to the model proposed:

(1) Interface design: addressing the steep initial learning curve raised by users, the interface is optimized to simplify the operation steps. More intuitive tutorials and explanations are introduced to help users grasp the model’s usage faster.

(2) Detail capturing: improvements are made to enhance the model’s learning ability for texture and color variations in images. By introducing finer convolutional layers and utilizing advanced activation functions in the CNN, the model’s accuracy in capturing subtle features in artworks is enhanced.

(3) Improvement in interactive performance: optimization is conducted on the system’s response speed, and the backend data processing flow is reconstructed to reduce user waiting time, providing a smoother interactive experience. More efficient algorithms are employed to accelerate data transmission and processing, ensuring timely real-time feedback.

Feedback data from real teaching environments where the model is used are collected through teacher and student interviews and observational studies. The analysis focuses on teaching methods, selection of teaching content, learning motivation, and satisfaction. Table 10 presents the qualitative analysis results of teaching effectiveness: Table 10 Qualitative analysis results of teaching effectiveness.

Interview/observation point	Description	Teacher feedback summary	Student feedback summary	
Teaching strategies	How teachers utilize model feedback to adjust teaching strategies	“The model feedback helps me better understand student needs, enabling personalized teaching content.”	“Teachers adjust teaching based on model feedback, making the course content more aligned with our interests.”	
Teaching content	Whether teaching content becomes more personalized due to model feedback?	“The model allows me to quickly adjust teaching content to cater to students’ varying levels.”	“The course content seems more diversified and better suits my individual learning needs.”	
Learning motivation	The impact of the model on student learning motivation	“Students show high interest in the interactive nature of the model, which enhances their learning motivation.”	“Through the model, I can see immediate feedback on my work, which boosts my motivation to learn.”	
Satisfaction	Student satisfaction with the teaching process and model usage	“Students generally express satisfaction with the model usage, believing it enhances classroom interaction.”	“I am satisfied with the model's immediate feedback and personalized suggestions.”	

This work designs and implements a before-and-after comparative experiment to monitor the changes in students’ creativity and technical proficiency in their works before and after using the model. Table 11 presents the assessment results of creative expression abilities: Table 11 Assessment results of creative expression abilities.

Assessment indicators	Average score before model usage	Average score after model usage	Improvement percentage (%)	Remarks	
Originality	3.5	4.7	34.29	Significant improvement in the originality of works	
Design complexity	3.2	4.4	37.50	Increased complexity and depth of designs	
Technical proficiency	3.8	4.6	21.05	Students’ increased proficiency in technical application	

The qualitative analysis and before-and-after comparative experiment results indicate that the improved CNN model combined with IoT technology has played a positive role in art and design education. Teachers can utilize the instant feedback provided by the model to adjust their teaching strategies, making the teaching content more tailored to students’ individualized needs, thereby enhancing the attractiveness and interactivity of teaching. Meanwhile, through interaction with the model, students can intuitively grasp artistic design concepts, thereby stimulating their creativity and critical thinking abilities. Additionally, there has been a significant improvement in students’ creative expression abilities, particularly in the originality and complexity of their works. These findings demonstrate that the model proposed enhances the information processing capabilities in art and design education and promotes the enhancement of students' creativity, offering a new teaching aid for the field of art and design education.

Discussion

The improved CNN model has yielded promising results in real-time monitoring of art design samples and student creative expression data. This model combines deep neural networks with techniques such as batch normalization layers and dropout layers, enhancing perceptual capabilities, feature extraction, and reducing overfitting.

The research results here are compared with previous literature. Fu et al. (2021) proposed a CNN-based basketball scoring detection method. They used deep neural networks to identify and track basketballs in images captured by cameras. This method employed layer-wise convolution and pooling operations to achieve accurate basketball position detection and automated scoring45. Zhang (2022) used CNN in conjunction with bio-image visualization techniques to analyze body variations in high-level dance movements. By training a deep neural network model, researchers could extract key frames from dance movements and analyze them using bio-image visualization techniques to delve deeper into the changes in dance movements46. In alignment with the investigations conducted by Fu et al. and Zhang, the current work delves into the processing of multi-modal data, specifically emphasizing the fusion of both image and sensor data. This reiterates the importance of embracing multi-modal approaches in the realm of art design education. Ghosh et al. (2021) evaluated the performance of state-of-the-art CNN architectures in Bengali handwritten character recognition. They compared various models based on recognition accuracy, training time, and model complexity and identified models with high accuracy and low training time, offering valuable insights for research in this field47. Marini et al. (2021) improved model classification accuracy by training models using a small amount of labeled local data and a large amount of unlabeled global data48. This work aligns with the research conducted by Marini et al., emphasizing understanding the model’s learning dynamics and adaptability. Diverse models showcase distinct dynamic changes throughout various iteration stages, offering valuable insights that can inform the development of a more intelligent and adaptable system for art design education. Liu et al. (2021) introduced a lightweight 3D CNN designed specifically for detecting deepfake images. This method effectively captured spatial and temporal information in deepfake images, providing a viable solution to the threat of deepfake technology49. In contrast to Liu et al.’s research, this work specifically emphasizes evaluating students’ creative expression abilities. The outcomes of the present work reveal distinct patterns in the influence of various models on these abilities. The RLET model initially displays suboptimal performance, progressively enhancing through iterative learning. Conversely, the MDNN model exhibits commendable performance in the early stages but undergoes a marginal decline in later phases. This highlights the nuanced strengths and weaknesses inherent in different models concerning their guidance on students’ creative expression. Chen et al. (2021) reviewed image classification algorithms based on CNN. They discussed the basic structure and principles of CNN and compared and analyzed different architectures, offering references for further research and applications50. In contrast to Chen et al.’s (2021) investigation into image classification algorithms, this work focuses on the performance of models in compressing art images. The results indicate a gradual improvement in the image compression quality of the three models with an increase in iteration cycles, demonstrating a significant enhancement in compression effectiveness during the learning and optimization process. Concerning student satisfaction, the IoT-CNN model exhibits higher performance and stability, receiving relatively higher satisfaction scores. However, the present work emphasizes the necessity for further research and practical applications to validate the applicability and effectiveness of these models in diverse educational settings. Fesseha et al. (2021) proposed a text classification method based on CNN and word embeddings suitable for low-resource languages. By constructing a CNN model and applying word embedding techniques, researchers achieved good results in text classification tasks for low-resource languages, providing new ideas and methods for research in this field51.

In summary, in terms of performance evaluation, different models show varying degrees of performance in terms of recommendation accuracy, security detection rate, image compression quality score, student satisfaction, and student creative expression ability score. The IoT-CNN model demonstrates better performance across multiple metrics and has the potential for application in art design education.

Conclusion

Research contribution

This work introduces an improved CNN model to enhance image processing and analysis accuracy and efficiency, thus optimizing the mode of art design education and resource allocation. The model achieves an enhancement in its feature extraction capability and a reduction in overfitting by increasing the number of convolution layers and neurons, incorporating batch normalization layers, and adding dropout layers. The main research contributions are as follows. (1) An improved model that integrates deep learning and IoT technology is proposed and validated, expanding their applications in the field of art education. (2) The model improves understanding and guidance of artistic creations and student creativity through the model. (3) The findings provide new insights and technological support for art design education. This work holds theoretical and practical significance and offers a viable solution for further promoting this teaching model.

Future works and research limitations

While this work provides an initial exploration of the application of the improved CNN in art design education, certain limitations require further improvement and expansion in future work. The main research limitations include the relatively small sample size and the relatively narrow range of art creation types, which may impact the model’s generalization capability. Future research could collect more diverse art creation samples for training and validation to broaden the model’s applicability. In future research endeavors, this work plans to expand the sample size and increase the diversity of artistic creation types to enhance the model’s generalization ability and practicality. Specifically, it aims to collect a wider range of art and design samples, including works from different artistic styles, various creative media, and diverse cultural backgrounds. Apart from enlarging the sample size, efforts will also be made to diversify the types of artistic creations. This includes but is not limited to paintings, sculptures, photography, digital art, and artworks from various art education settings. In order to address the challenge of insufficient sample data, the work intends to introduce transfer learning techniques. By utilizing neural network parameters pre-trained on large-scale databases like ImageNet as the initialization parameters for the model, it can fine-tune the model to adapt more quickly to new types of artistic creations. Additionally, the work will actively seek collaboration with industry partners such as art schools and art education institutions to acquire more diversified art and design samples. This includes not only works from various artistic styles but also artworks created in different environments, enabling the model to better understand and analyze the characteristics of artistic creations in different settings. Based on the broadening of the scope and types of samples, the practicality of the model can be tested in diverse educational environments, ensuring its effectiveness in various educational contexts and artistic creation scenarios.

Author contributions

Bo Liu: conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing—original draft preparation, writing—review and editing, visualization, supervision, project administration, funding acquisition.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author Bo Liu on reasonable request via e-mail liubo15153141697@163.com.

Competing interests

The author declares no competing interests.

Ethics statement

The studies involving human participants were reviewed and approved by University of PERPETUAL HELP System DALTA Ethics Committee (Approval Number: 2022.59485). The participants provided their written informed consent to participate in this study. All methods were performed in accordance with relevant guidelines and regulations.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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