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

39223236
71536
10.1038/s41598-024-71536-7
Article
Optimization of the convolutional neural network classification model under the background of innovative art teaching models
Xu Xi 1
Xu Shuguang xisguangx@163.com

2
1 https://ror.org/05x510r30 grid.484186.7 0000 0004 4669 0297 Fuzhou Institute of Technology, Fuzhou, China
2 https://ror.org/03cq4gr50 grid.9786.0 0000 0004 0470 0856 Faculty of Fine and Applied Arts, Khon Kaen University, Khon Kaen, Thailand
2 9 2024
2 9 2024
2024
14 203253 2 2024
28 8 2024
© The Author(s) 2024
2024
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To improve students’ ability to recognize and appreciate artworks, and further enhance their academic performance and classroom satisfaction, this study explores the application of the Convolutional Neural Network (CNN) model based on optimization in art teaching. Firstly, the importance and challenges of art teaching are analyzed. Secondly, the principle and structure of CNN and its application in the classification field are expounded, and then the CNN classification model is optimized. Finally, the effectiveness of the optimized model is verified by experiments. Experimental results show that the optimized model’s accuracy is up to 95.2% in the performance evaluation. The training time of the optimized model is much lower than that of the traditional model, and this model still maintains 95.2% accuracy under the noise of 14.7%. In addition, the accuracy of the optimized model on the unseen test data is 92%. In comparing teaching experiment results, by introducing the CNN classification model, Class B students’ average score of art homework has increased by 4.3 points. The score for class satisfaction is 8.1 points. This indicates that the optimized CNN model has significant advantages in art teaching and can effectively improve students’ classroom satisfaction and academic performance. Therefore, this study has specific reference significance for the innovation of the art teaching model.

Keywords

Convolutional neural network
Classification model
Art teaching
Teaching mode innovation
Classroom satisfaction
Subject terms

Computer science
Scientific data
issue-copyright-statement© Springer Nature Limited 2024
==== Body
pmcIntroduction

Research background and motivations

In the current field of education, the rapid development of technology, especially breakthroughs in artificial intelligence (AI) technology, has begun to influence educational methods and learning experiences profoundly. Especially in art teaching, traditional teaching methods are facing the need for innovation to adapt to the challenges and opportunities of the digital age1–3. As an advanced deep learning (DL) technique, the Convolutional Neural Network (CNN) has made remarkable achievements in image processing and visual recognition. Its application potential in art teaching has attracted wide attention from educators and researchers4. With the increasing application of digital technology in art creation, traditional art teaching models need repositioning. By combining CNN’s powerful image processing and analysis capabilities, it is expected to develop more personalized, interactive, and innovative teaching methods5. This helps improve students’ artistic understanding and creative ability and promotes their adaptation and mastery of new technologies, laying a solid foundation for their art creation in the digital world.

At present, the field of art education is faced with the urgent need to integrate new technologies. With the rapid development of AI and machine learning (ML) technology, especially the significant progress made by CNN in image processing and recognition, this study believes that applying these technologies to art education is an innovative and forward-looking initiative. Moreover, traditional art teaching methods have limitations in certain aspects, such as improving students’ understanding of the style of artworks and their ability to imitate them. By introducing CNN, students can gain a deeper understanding of the structure and style of artistic works, thereby improving teaching effectiveness. Meantime, art displays skills and is an important way of innovative thinking and personality expression. Combined with CNN, students can be inspired with new perspectives on the understanding of arts, and foster their innovative thinking and experimental spirit, which is essential for art education in the twenty-first century. This study provides a successful case of an interdisciplinary teaching model by integrating computer science and art education. This model could inspire other subject areas to explore new teaching methods that combine technology with expertise, such as integrating data analysis into the social sciences or applying ML to biological research.

Research objectives

This mixed study aims to innovate the art teaching model based on the CNN classification model. The following goals are achieved through innovative research of art teaching models based on the CNN classification model. First, the characteristics and shortcomings of the existing art teaching modes are analyzed and summarized, and the innovation needs and challenges are clarified. Second, an optimized CNN model is designed and implemented to realize the automatic classification and evaluation of artworks and apply it in specific art teaching scenarios. Third, the innovative strategy of art teaching mode based on the optimized CNN model is explored and formulated to promote the development of personalized teaching and interactive teaching. Lastly, suggestions for further improvement and development are proposed, providing a reference for art education’s innovation and sustainable development.

By achieving the above research objectives, this study furnishes theoretical support and practical guidance for the innovation and optimization of art teaching mode. Besides, it positively contributes to cultivating students with artistic literacy and creativity.

Literature review

This study discussed the current situation and development trend of art teaching mode innovation research based on the CNN classification model through a comprehensive analysis of relevant research literature.

In previous studies, Li et al.6 realized the automatic classification of different types of artworks by using CNN. By training the model, they successfully divided paintings into different categories, such as landscape, portrait, and abstract art, and achieved a high classification accuracy. Pham et al. (2021) applied the CNN model in art teaching to evaluate and appreciate artworks. They used the model to automatically extract the features of painting works and evaluate them according to painting composition, color application, and other aspects, offering objective guidance for teachers7. Wang et al. (2023) studied the innovative method of the art teaching mode by CNN. They designed a multi-layered CNN structure, applied it to art evaluation, and compared it with traditional scoring methods. The research results showed that the evaluation method based on CNN was more accurate and reliable8. Sethi and Jaiswal (2022) established an innovative practice platform for the CNN-based art teaching mode. By building a virtual art classroom environment, they provided immediate guidance and evaluation of students’ painting process with the help of the CNN model, which improved students’ learning effect and personalized guidance quality9. Abdel-Salam et al. (2022) explored the application of CNN in art creation. They analyzed artists’ painting styles through the CNN model and provided a reference for students to promote communication with traditional artists during creation10. Guo et al. (2022) found that the innovation of an art teaching mode based on CNN can give more personalized guidance and feedback. They adopt the CNN model to analyze and evaluate the artworks independently created by students and provide targeted guidance according to students’ characteristics and needs to help them improve their creative level11. Parmar and Morris (2021) focused on using CNN to realize the style transfer of artworks. By training the model to learn the painting styles of different artists, they successfully converted the style of one image into the specific style of another artist, expanding students’ creative ideas and artistic expression12.

The traditional research denoted that the traditional art teaching model has limited teacher resources and cannot conduct detailed analysis and evaluation of artworks for each student, and the classification and evaluation of artworks lack objectivity and standardization. In this study, CNN technology is systematically applied in the field of art teaching. This integration can improve the accuracy of art analysis and provide students with a new way to understand data-based art. This approach introduces quantitative analysis into teaching, offering students insight into artistic styles, techniques, and evolution.

Research model

The importance and challenge of art teaching

Art teaching is critical in cultivating students’ aesthetic ability, artistic accomplishment, and creativity13. Through art teaching, students can learn painting skills, design concepts, art history knowledge, etc. Meanwhile, they can develop their powers of observation, expression, imagination, and critical thinking14,15. Art education can cultivate students’ perception ability and artistic thinking mode, improve students’ perception and appreciation of beauty, and enrich their humanistic quality and aesthetic taste16. However, art teaching also faces a series of challenges. The details are shown in Fig. 1:Fig. 1 Challenges in art teaching.

At present, art teaching adopts multiple different teaching modes to meet the diverse needs of students and educational environments17. The details are exhibited in Table 1: Table 1 Common art teaching modes.

Teaching modes	Content	
Traditional teaching mode	Teachers, as the center, pay attention to teaching skills and imitating works. Teachers usually teach drawing skills and artistic knowledge through demonstrations, explanations, and instruction. Students practice and create under the guidance of teachers, and attach importance to the cultivation of basic painting skills	
Individual coaching mode	This mode focuses on the needs and potential of individual students. Teachers work with each student one-on-one or in small groups, providing personalized guidance and feedback based on the student’s interests and characteristics	
Collaborative learning mode	It encourages student collaboration and focuses on their interaction and teamwork. Students learn from each other by co-creating, sharing, and exchanging their ideas and experiences. This mode fosters students’ communication and collaboration skills and stimulates innovative thinking and collective intelligence	
Technical support mode	This mode uses modern technology as an auxiliary tool to offer technical support, such as electronic painting and virtual reality. Students can create through electronic devices and use multimedia and Internet resources for study and research	
Feedback oriented mode	It focuses on teachers’ evaluation and feedback of students’ artworks. Through detailed comments and guidance, teachers help students identify problems, improve their work, and make specific suggestions and goals	

It should be noted that different teaching modes may affect diverse educational backgrounds and student groups18–21. The selection of art teaching mode should comprehensively consider teaching objectives, student needs, teaching resources, and other factors, and flexibly apply the corresponding teaching mode to achieve a more effective art education22. Moreover, with the progress of science and technology and social changes, it is also an essential direction for the development of art education to explore innovative teaching modes constantly19.

The principle and structure of CNN and its application in the field of classification

CNN is an artificial neural network model based on DL, with powerful image processing and pattern recognition capabilities23. It simulates the working principle of biological vision systems and extracts image features through multi-layer convolution and pooling operations24–26. The basic principle and structure of CNN are drawn in Fig. 2:Fig. 2 Principle and structure of CNN.

CNN consists of an end-to-end neural network structure through the convolutional, activation, pooling, and fully connected layers27. It can automatically learn features in images and optimize parameters in the training process, to achieve various visual tasks such as image classification, target detection, and image generation28–31. CNN has a comprehensive and vital application in the domain of image classification. It can classify images efficiently and accurately by automatically learning the features and patterns of images32. The specific application is plotted in Fig. 3:Fig. 3 Application of CNN in the field of classification.

The application of CNN in the field of image classification has achieved great success33. It realizes image classification tasks with high accuracy and robustness by automatically learning image features and patterns. With the continuous progress and development of CNN technology, it is believed that its application in the image classification field will become more extensive and mature, and bring more innovation and development opportunities to various industries34–36.

There are many applications of CNN in learning art, and CNN can be employed to identify and classify the styles of artworks. By training the network to recognize different genres of art, CNN can help students and researchers quickly and accurately identify stylistic features of works. In addition, CNN can analyze the artwork’s details, such as brush strokes, color use, and composition methods, to provide a deeper understanding of the artwork. Besides, CNN can assist artists and students in their creations. For example, by analyzing a large number of artworks, CNN can generate style guides to help artists or students mimic a particular artistic style. In addition, CNN can also be used to generate creative inspiration, affording new visual elements and composition suggestions by learning different art styles and techniques. In the field of education, CNN can serve as a powerful tool to teach art history and theory. It helps students understand the artistic styles and techniques of different historical periods through visual analysis. Furthermore, CNN can also be utilized to evaluate students’ work, providing feedback on style, technique, and originality. CNN can be used for the restoration and preservation of artworks. By analyzing the style and technique of the damaged part, CNN can recommend possible fixes or generate visual simulations of the restored work. This application is of great value for the preservation of cultural relics and the study of art history.

Overall, the diversified use of CNN in art learning and application promotes technological innovation in art and provides new perspectives and tools for art education and research.

Optimization strategy of the CNN classification model

Model training and optimization are crucial in the CNN application37. Through reasonable training and optimization strategies, the performance and generalization ability of the model can be improved to the greatest extent38–41. The optimization strategy of this study is outlined in Table 2: Table 2 CNN optimization strategy.

Strategy	Method	
Weight initialization	Appropriate weight initialization can accelerate the convergence of the model and improve its generalization ability. The commonly used weight initialization methods include random initialization, Xavier initialization, and others	
Learning rate scheduling	The choice of learning rate will directly affect the convergence speed and performance of the model. Common learning rate scheduling strategies involve fixed learning rate, dynamic adjustment, learning rate attenuation, etc	
Regularization	L1 and L2 regularization constrain the complexity of the model by adding the corresponding regularization term penalty weight to the loss function	
Batch normalization	Batch normalization normalizes the input features at each layer, resulting in similar mean and variance for each feature dimension. This helps to accelerate the model convergence, stabilize the training process, and improve the model’s generalization ability	
Ensemble learning	Common ensemble learning methods cover bagging, boosting, and more. For the CNN model, multiple independent models can be trained to vote or average prediction results during testing to improve the accuracy and robustness of the model	
Parameter tuning	By carefully adjusting the model parameters of the optimization algorithm, such as hyper-parameter, layer number, and neuron number, better tradeoff points can be found, and the model’s performance can be enhanced	
Model complexity control	Overly complex models can lead to overfitting problems, while extremely simple models may not adequately learn features and patterns in the data	

The CNN classification model is optimized according to the optimization strategy.

Optimization structure of the CNN classification model

Combined with the optimization strategy proposed in this study, the CNN classification model is optimized. The architecture of the optimized CNN classification model is suggested in Fig. 4:Fig. 4 The architecture of the optimized CNN classification model.

Through the integration and application of the above optimization strategies, a more powerful and stable CNN model can be established. The model starts at the input layer, and then the data passes through multiple convolution layers, each followed by a pooling layer. The convolutional layer is responsible for extracting the features of the input data, while the pooling layer is used to reduce the spatial size of the features, thus reducing the number of parameters and the amount of computation. This is followed by a batch normalization layer, which is employed to accelerate the training process and improve the stability of the model. The data then enters the fully connected layer, which is a layer that maps learned “high-level” feature representations to the sample label space. After the fully connected layer, the Dropout layer is set to prevent overfitting. Lastly, the Softmax activation function is used to output the class probability. The model also includes loss functions to calculate prediction errors, optimization algorithms to adjust weights, learning rate scheduling, and regularization techniques to further improve the model’s generalization ability.

Experimental design and performance evaluation

Datasets collection

The dataset selected for this study is the Modified National Institute of Standards and Technology (MNIST) dataset. The MNIST dataset contains 60,000 handwritten digital images for training and 10,000 for testing. Each image is a grayscale image with a size of 28 × 28 pixels. Each image is labeled with a corresponding label that represents the handwritten digits in the image. The MNIST dataset is widely used to evaluate and compare the performance of machine learning and DL models. It is also regarded as learning computer vision in one of the most basic and classic datasets, which can be through the website (download website: http://yann.lecun.com/exdb/mnist/). Another dataset selected for the experiment is the Art Painting Dataset, which contains more than 50,000 works of art, grouped by artist, style, and period. The dataset is useful for studying tasks such as the classification of works of art, the identification of artistic styles, and the identification of artists. The dataset can be downloaded from the official website: (https://sammlung.staedelmuseum.de/en?gad_source=1&gclid=Cj0KCQjwv7O0BhDwARIsAC0sjWMmC3nhr2Uek3EL5WIzIq-onrvG1OIXD91a6Cj4pXv_A8FawzEa6-saAtWZEALw_wcB).

Experimental environment and parameters setting

The experimental environment is displayed in Table 3: Table 3 Experimental environment.

Device type	Configuration	
Processor	Inter(R) Xeon(R) CPU E5-2620 v4 @ 2.10 GHz	
GPU (Graphics processing unit)	NVIDIA Titan Xp 12 GB	
Memory	128 GB	
Operating system	Ubuntu 16.04 LTS	

For the study’s accuracy, the experiment’s parameters need to be unified. Among them, the convolution kernel size is set to 3 × 3, the number of convolution kernels is 256, the pool size is 2 × 2, and the batch size is 16. The step size, learning rate, and Dropout rate are set to 1, 0.001, and 0.3. The activation function is Relu.

Performance evaluation

Performance evaluation of the CNN optimization model

Experimental comparison models are Support Vector Machine (SVM) and Decision Tree (DT). The indicators of experimental comparison include accuracy, training time, robustness, and model generalization ability. The experimental results are denoted in Fig. 5:Fig. 5 Performance evaluation comparison (a) Accuracy (b) Training time (c) Robustness (d) Generalization ability.

Figure 5 demonstrates that the optimized model has significant advantages under the comparison of accuracy, model generalization ability, training time, and robustness. As the number of input images increases, the benefits become more apparent. In terms of accuracy, the optimized model achieves a maximum accuracy of 95.2%, which is much higher than traditional models. In terms of training time, when the number of images reaches 400, the training time of the optimized model only takes 15.3 s. With the growth of the number of images, this model’s training time increases much faster than that of traditional models. In comparing robustness, the proposed model maintains 95.2% accuracy under 14.7% noise, which is more robust than SVM and DT. This shows that the proposed model can maintain good prediction ability in the presence of noise or outliers. Finally, regarding model generalization ability, the proposed optimized model’s accuracy on unprecedented test data is 92%, superior to SVM and DT. This illustrates that the proposed model has optimal generalization ability and can better adapt to unknown data samples.

Analysis of the innovation effect of art teaching mode

In the experiment, two classes with similar art scores in school A are selected for research, with 52 students in each class. The teaching mode of Class A is traditional, based on teacher-centered teaching methods, emphasizing the training of basic skills and paying attention to students’ understanding and mastery of art knowledge. In this mode, students usually learn art by listening to lectures, taking notes, imitating demonstrations, and practicing. The teaching mode of Class B is to encourage students to provide their favorite image works and use the model for image classification and evaluation. In this way, the characteristics of students’ preferred artworks are extracted and analyzed to further understand their creative style and performance. According to the students’ personality characteristics and creative style, art teaching should be targeted. After a one-month experiment, students’ and class satisfaction scores are obtained through exams and questionnaires. The specific results are listed in Table 4: Table 4 Comparison of student performance and classroom satisfaction.

	Class A	Class B	
Average score of art homework before the experiment (out of 100 points)	73.8 points	74.2 points	
Average score of art homework after the experiment (out of 100 points)	74.1 points	78.5 points	
Average score of classroom satisfaction before the experiment (out of 10 points)	6.7 points	6.5 points	
Average score of classroom satisfaction after the experiment (out of 10 points)	6.6 points	8.1 points	

According to the data in Table 4, it can be observed that the average score for art homework of Class B has increased by 4.3 points, while Class A students’ average score for art homework under the traditional teaching mode has hardly changed. This manifests that by introducing the CNN classification model, Class B students have made apparent progress in art homework, while the traditional teaching mode has failed to bring similar effects. Moreover, in the comparison of classroom satisfaction, Class B and Class A students’ satisfaction scores reach 8.1 and 6.6, respectively. This means that introducing the CNN classification model can effectively improve the achievement of art teaching and classroom satisfaction.

Comparison and analysis with other neural network models

To further verify the effectiveness of the proposed optimized model, the study compares it with other neural networks using the Art Painting Dataset. The models chosen for the experiment are Recurrent Neural Network (RNN) and Multilayer Perceptron (MLP). RNN is a type of neural network with a cyclic structure that can process sequential data. It captures dynamic information from time series or sequential data through recursive computation of hidden states. MLP is a feedforward neural network, typically consisting of an input layer, one or more hidden layers, and an output layer. Each neuron in one layer is fully connected to all neurons in the previous layer. The experimental results are given in Table 5: Table 5 Experimental comparison results.

Model	Accuracy (the test set)	Accuracy in noisy environments	Accuracy for unseen data	Robustness (Accuracy of adversarial samples)	Anomaly detection capability (accuracy)	
The optimized CNN model	95.2%	95.2%	92%	85%	88%	
The RNN model	93.5%	92.8%	90%	80%	85%	
MLP model	90.2%	88.5%	85%	75%	80%	

Table 5 denotes that the optimized CNN model performs excellently across multiple performance metrics. The accuracy on the test set reaches 95.2%, with the accuracy in noisy environments still maintains at 95.2%, and the accuracy for unseen data is 92%. In terms of security, the optimized CNN model’s accuracy against adversarial samples is 85%, and its anomaly detection capability accuracy is 88%. In contrast, the RNN model’s accuracy on the test set is 93.5%, with the accuracy in noisy environments at 92.8%, and the accuracy for unseen data at 90%. Its robustness against adversarial samples is 80%, and its anomaly detection capability is 85%. The MLP model’s accuracy on the test set is 90.2%, with the accuracy in noisy environments at 88.5%, and the accuracy for unseen data at 85%. Its robustness against adversarial samples is 75%, and its anomaly detection capability is 80%.

To more comprehensively evaluate student satisfaction and classification accuracy, the study uses a scoring system with a full score of 5, focusing on student engagement and emotional investment as indicators. The experimental results are shown in Table 6: Table 6 Score results.

Indicator	The optimized CNN model	The RNN model	The MLP model	
Student engagement score	4.5	4.2	3.8	
Emotional investment score	4.6	4.3	4.0	

Table 6 reveals that the optimized model performs excellently in terms of student engagement and emotional investment. The student engagement score is 4.5, and the emotional investment score is 4.6, indicating that students show high enthusiasm and strong interest in the course content during classroom activities. In comparison, the RNN model scores 4.2 and 4.3 on these two indicators, respectively, while the MLP model scores 3.8 and 4.0, respectively. Analysis of the results for student engagement and emotional investment shows that the optimized CNN model not only improves students’ academic performance in art education but also significantly enhances their classroom engagement and emotional investment. High engagement scores suggest that students exhibit high enthusiasm in classroom discussions, completing assignments, and participating in extracurricular activities. This is closely related to the optimized CNN model’s ability to offer more accurate and intuitive classification and recognition results for artworks, which increases students’ interest and investment in the course content. The high scores for emotional investment indicate positive emotional responses from students toward the art course, showing that the optimized CNN model plays a vital role in increasing students’ interest in the course content. In contrast, although the RNN and MLP models also enhance student engagement and emotional investment in some aspects, their overall performance is not as good as the optimized CNN model. This may be due to the optimized CNN model’s superior performance in image classification and recognition, making the teaching process more intuitive and engaging, thereby attracting students’ attention and interest more effectively.

Discussion

After performance evaluation experiments, the optimized CNN model in this study significantly surpasses the traditional model. The experimental results show that the accuracy of the model reaches 95.2% on certain datasets. When processing 400 input images, the model is trained in 15.3 s and maintains the same accuracy with 14.7% noise interference. When dealing with unknown test data, the accuracy of the model is 92%. This performance improvement is attributed to the adoption of parametric optimization algorithms and refined training strategies. It includes hyperparameter tuning, learning rate optimization, and effective regularization techniques to enhance the model’s generalization ability and maintain robustness in noisy and outlier environments. This proves the adaptability and superiority of the model in dealing with input variability. In the experiment of two classes of school A, it can be seen that the average score of art homework of Class B experimental group students is improved by 4.3, and the satisfaction score is 8.1. This is because the CNN classification model can provide more accurate and detailed feedback and assessment, and help students better understand and master the main points and techniques of art homework. Through the assistance and guidance of the model, the students of Class B can identify and analyze the artworks more accurately, thus obtaining higher marks in the homework. At the same time, by introducing the CNN classification model, students can experience their progress and sense of achievement more intuitively. This is conducive to stimulating students’ learning motivation, improving their interest and investment in arts, and further promoting the improvement of their academic performance. The research shows that the application of the optimized CNN model in art teaching can significantly improve students’ academic performance and classroom satisfaction. Analysis of the experimental results illustrates that the optimized CNN model exhibits significant advantages in multiple aspects. First, the high accuracy of the optimized CNN model on the test set indicates its strong classification capability, effectively identifying and distinguishing different categories of artwork. Second, the maintained accuracy in noisy environments shows that the optimized CNN model has strong anti-interference ability, functioning stably in complex environments. Additionally, the high accuracy for unseen data suggests that the model has good generalization ability, capable of handling new samples not seen during training. In terms of security, the optimized CNN model outperforms the RNN and MLP models against adversarial samples, showing high robustness. This means that the model can maintain high classification accuracy even when facing malicious interference, thus enhancing its reliability in practical applications. Moreover, the optimized CNN model also excels in anomaly detection, effectively identifying and handling abnormal data, further facilitating the model’s security and usability. Overall, the optimized CNN model demonstrates the most significant application effects in art education, improving students’ academic performance and classroom satisfaction while providing higher security guarantees. Compared with other neural network models, the optimized CNN model exhibits comprehensive advantages in accuracy, robustness, and anomaly detection capability, offering important technical support and a theoretical basis for innovative art teaching models.

This finding echoes the research of Li et al.42 and Pham et al.43, who also found that CNN showed excellent performance in the classification and evaluation of artistic works. In addition, the research results of this study further confirm the application potential of CNN in the field of education, especially in personalized learning and teaching quality improvement. Although this study has achieved positive results, there are still some limitations. The research mainly focuses on the technical performance of the CNN model but ignores the influence of psychological and educational factors such as students’ learning motivation and creativity. The findings of this study are consistent with the research of Guo et al.44, who also pointed out that the teaching method based on CNN could provide more personalized guidance and feedback. In addition, the research of Parmar and Morris45 showed that CNN could be used to transform the style of artistic works, which showed the potential of CNN in promoting students’ artistic creativity. Future research will continue to combine psychology and pedagogy to explore how the CNN model affects students’ learning motivation, creativity, and artistic appreciation.

Although techniques such as style transfer and AI generation have garnered widespread attention in the field of art and have demonstrated their potential in creation and teaching, this study chooses to adopt CNN for reforming art education due to its rationality and unique advantages. The primary goal of this study is to enhance students’ ability to recognize and appreciate artworks, thereby improving their academic performance and classroom satisfaction. CNN excels in image classification and recognition, accurately identifying and classifying artworks of different styles and categories, which aligns with the teaching objectives. In contrast, style transfer and AI generation technologies are more focused on art creation and generating new works, which do not completely align with the goal of enhancing students’ appreciation and recognition skills. Furthermore, CNN in the field of image recognition and classification is highly mature, with numerous successful applications and optimization strategies. Choosing a mature technology ensures stability and reliability in teaching reforms, reducing risks in experimentation and application. Moreover, while style transfer and AI generation technologies are highly innovative, their application in teaching is still in the exploratory stage, with their practical effects and adaptability to teaching yet to be fully validated.

Conclusion

Research contribution

The main contributions of this study are as follows:This study proposes an innovative art teaching model based on the optimized CNN model, which is introduced into art teaching to improve students’ ability to recognize and appreciate artworks. Compared with the traditional teaching model, this model can offer more detailed and accurate feedback and guidance through image recognition and analysis, to help students better understand and master artworks.

The performance of the optimized model is analyzed and compared. Through experimental data and detailed analysis, the performance of the optimized CNN model in terms of accuracy, training time, robustness, and model generalization ability is evaluated and compared. The results reveal that the optimized model has obvious advantages on these indicators, with higher accuracy, shorter training time, stronger robustness, and better generalization ability.

The application effect of the optimization model in art teaching is evaluated in practice. This study applies the CNN model based on optimization in the art classroom of a middle school and carries out a practical evaluation. The results indicate that by introducing the model as an auxiliary tool, students’ artwork recognition ability and appreciation level have been remarkably enhanced, and the average score of art homework and class satisfaction have been significantly improved.

Useful exploration and reflection on the innovation of teaching models in the field of art education are also provided. This study offers practical experience and theoretical support for the innovation of teaching models in art education. The innovation of the art teaching model based on the optimized CNN model can provide students with a more intuitive and personalized learning way and promote their ability to recognize and appreciate art.

This study also offers an opportunity for teachers to have a more comprehensive understanding of students’ learning situations and needs to better guide and tutor them.

In short, the contribution of this study is to propose and apply the innovation of the optimized CNN model-based art teaching mode and analyze and evaluate the performance of the optimized model. Through practical evaluation and exploration, this study offers valuable experience and thinking for the innovation of teaching mode in the art education field, and has vital practical significance for improving students’ ability of art recognition and appreciation.

Future works and research limitations

Although the CNN model’s application has been preliminarily explored in art teaching, the in-depth exploration and optimization of the model performance are still in the initial stage. Future research should consider using diverse network architectures, algorithms, and parameter tuning to enhance the model’s accuracy and robustness. In addition, the sample size of the experimental study is limited to a specific educational environment, and it needs to be extended to a broader educational background and a larger sample size to verify the broad applicability of the model. Further, an interdisciplinary research approach will help deepen the application of the CNN model in art teaching, especially how it affects students’ learning motivation and creativity development. Future work should also encompass collecting feedback from teachers and students to provide an empirical basis for innovation in fine arts education.

Author contributions

X.X.: Conceptualization, methodology, software, validation, formal analysis, writing—original draft preparation S.X.: investigation, resources, data curation, 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 on reasonable request.

Competing interests

The authors declare no competing interests.

Publisher's note

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