# Painting Modeling Language Based on Convolution Neural Networks in Digital Media Art

Authors: Lingyan Zhou
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

Digital media, as a high-tech method, has gradually affected all aspects of social production, including artistic creation, in the information age. Contemporary painting art, as an important form of artistic expression, will inevitably be baptized by digital media in the tide of time. The color of digital media ushers in a new era in contemporary painting. The development of deep learning makes the research field of artificial intelligence move towards a deeper and more practical level. Among them, CNN (Convolutional Neural Network) has very important research value in image feature expression. The digitization of painting works is of great significance to the effective use of painting resources. The traditional image classification methods do not consider the subjective characteristics of painting works, and most of the features need to be extracted manually, resulting in the loss of detailed features. The research and design of image retrieval system in this paper is based on CNN. One is to extract the features of the image using convolution neural network. The scale of image data sets in the resource database is still small. For the field of deep learning research, data sets can only be regarded as small-scale data sets. Combined with the actual situation, the convolution neural network model is trained, and the better convolution neural network structure Artnet is selected through experimental analysis. It is a seven-layer network structure including two volume layers, two pool layers, and a full connection layer. The second is to design and implement a practical image retrieval system based on Artnet and effectively integrate it with other systems of the project as an important function of the project construction.


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## Body

## 1. Introduction

In modern painting, the creation of distinctive artistic images is inseparable from the painter’s rational use of painting language and careful training [1]. The formal beauty of modeling language and the diversified expression trend constitute the blood, cells, muscles and bones, and appearance of the picture life, which makes the work have life vitality and moving artistic appeal [2]. The combination of computer technology and art makes the traditional foundation of painting and modeling feel pressure. But at the same time, the collision process between the basis of painting modeling and digital media art is always integrating and perfecting each other [3]. The basic modeling of painting has a profound impact on the artistic creation of digital media [4].Painting is an important carrier for human civilization’s development, and studying it will help people better understand human history and culture [5]. Researchers now have access to a large number of digital painting resources thanks to the rapid digitization of western oil paintings, oriental landscape paintings, and other painting works, as well as the establishment of a painting electronic library. To make effective use of these resources, the computer must be able to accurately classify painting resources for the researchers’ convenience [6]. Painting images are artificial works with artistic style features such as painter’s style and art school, and their content is quite different from reality [7]. Natural images primarily describe the real scene objectively, and their content is little different from reality. Traditional image classification methods ignore these features of paintings, and there are a large number of complex features that must be extracted based on personal experience, resulting in easy loss of detail features and poor model generalization [8]. Therefore, the classification of painting images is more challenging. From the perspective of computational aesthetics, the aesthetic quality of the image is independently evaluated through a calculable method. In order to screen out high-quality images, the aesthetic features of images were extracted manually in the early stage, and then, the traditional classifier was used to classify the high and low aesthetic feeling of images [9] . Image aesthetic recognition is based on art as the main body. Through the recognition of images, the computer simulates the human brain to judge and recognize the beauty of images. Both image recognition [10] and image aesthetic recognition belong to the category of biometric recognition. However, because aesthetic perception is affected by personal subjectivity, cultural preferences, etc., subjective evaluation factors need to be considered when studying image aesthetics. In this paper, in order to solve the many problems existing in the traditional image classification method to classify painting images and improve the classification effect of painting images, based on the classic CNN [11], a method of using CNN to classify painting images is proposed. By analyzing the influence of the size of the convolution kernel, the width of the CNN structure, the number of training samples on the classification results, and the network structure and parameters are optimized [12].Researchers use convolution neural network to evaluate image aesthetics based on image global, local, and personalized query [13]. As the representative of artificial intelligence and machine vision, robot not only realizes intelligence but also can observe and judge things [14]. Deep learning has made outstanding contributions to the development of computer vision, and one of the important factors is the continuous innovation of activation function. [15].This paper studies image classification on the basis of CNN, and further studies the influence of activation function on the accuracy of image classification, proposes a combination of linear and nonlinear function as the activation function, and uses it on the Chinese painting data set and general. The experimental results show that the improved activation function improves the classification performance [16].In the era of digital media, artists’ creative methods are no longer limited to traditional techniques. The development of digital media technology provides extensive technical support for the creation of contemporary painting art: the development of computer graphic software provides art creators with more traditional colors than in the past. In the color selection of color gamut, the emergence of microinjection technology enables artists to use electronic devices to create color works on drawing paper with high fidelity, photography, printing, and a huge communication medium that transmits information over long distances to the general public, especially contemporary. The artist brings a contemporary visual experience. All of the above indicate that the development of digital media itself provides a prerequisite for changing the appearance of contemporary paintings.


## 2. Related Work

Reference [17] developed and designed a multilayer artificial neural network by making up and perfecting the modern structure of CNN through research. [18] Literature Direct supervised deep network training is possible with the new activation function ReLU, but the ReLU activation function has some drawbacks, such as neuron death. AlexNet, as described in reference [19], uses big data for training and a graphics processing unit (GPU) for accelerated computation. Simultaneously, the application of Relu and Dropout to the CNN model is proposed. Reference [20] proposed using a back propagation algorithm to train a multilayer artificial neural network called Lenet-5. GoogleNet [21] primarily investigates the impact of convolution neural network width on image classification accuracy, proposes an incident structure to increase network width, and uses 1 1,3 3,5 5 convolution kernels rather than 7 7 convolution kernels, and finally designs a 22-layer depth neural network structure. It has a good classification effect, according to the results of the experiments. To improve the effect of target recognition in painting images, Reference [22] uses CNN to learn the method of target recognition in natural images. According to the literature [23], a CNN method based on hybrid sparseness was proposed to automatically extract the stroke features of Chinese ink paintings and classify them according to the author, with good classification results. AlexNet was developed using deep learning models and applied in the field of image classification, according to literature [24]. Literature [25] is an objective and dialectical analysis of ink art and digital media through the study of the diverse development of ink art in digital media, the benefits and drawbacks of media convergence, and the prospects for future growth. Literature [26] summarizes the artistic characteristics of design ideas and displays methods under the influence of digital technology by analyzing specific examples, while retaining the characteristics of traditional calligraphy and painting, and it also provides people with a better experience.It can be seen from the above literature that based on the classical Lenet-5 network model, this paper determines the convolution neural network model suitable for judging image aesthetic classification by changing the convolution layer and full connection layer in the convolution neural network and performing model training and result test on the femaleface data set. It can be applied to image classification, target detection, and other fields as a supervised learning method.


## 3. Research on the Application of Digital Media Art and CNN in Painting Creation

### 3.1. The Positive Influence of Digital Media Art Design in Painting Creation

Painting art and digital media design are developing together, but their development has different characteristics. Excellent paintings should have vitality and collection value, which cannot be copied. They are not only scientific research but also ornamental. However, digital media is different. Its biggest feature is its reproducibility. It can not only copy indefinitely like printed matter but also make different modifications every time. However, because digital media design is reproducible, the homogenization and plagiarism in its design process are very serious. Painting creation can improve this problem. By adding painting elements to the design process, you can make your works innovative and different. Digital media is developed by relying on information technology. It can make painting more characteristic of the times. It can be said that it has changed a new coat for painting. The flexibility and quickness realized by painting can inject new energy into the artistic brilliance of digital media design. The application of painting creation can make the design of digital media vivid, with unique picture quality. Many children like hand-painted animation more than those produced by digital media. Hand-painted animation integrates the ideas of many authors and is close to children’s psychology. However, although the hand-painted animation is lifelike, it cannot draw the effect of computer digital media. The painting works are strengthened on the basis of computer processing, and various strengthening is carried out according to the design of digital media. By changing the characteristics of strengthening painting for one point and connecting all points in the time period, the painting can be innovated in multiple dimensions. Under the impact of various science and technology in the Internet era, people’s lifetime arrangement began to develop in the direction of fragmentation. The colors of Gerhard Richter’s paintings are based on multiple sources, such as newspapers, books, magazines, and personal photos. Gerhard Richter uses black and white and color photos, or illusory image effects produced by overexposure, underexposure, etc., to form nonemotional, rational, calm, and restrained color picture effects, leading a new aesthetic taste. The development of photographic media technology is an opportunity given to artists by the times. Gerhard Richter uses the picture effects of photographic media to express the artist’s pursuit and challenge of the essence of art, seeking a balance between the development of art itself and the development of society, and he created his paintings with unique colors. The process of digital information acquisition, editing, and processing is shown in Figure1.Figure 1
Process diagram of digital information acquisition, editing, and processing.Digital media technology can provide people with massive information every day, so as to achieve ideal advertising effect. However, the current mode of digital media communication makes it difficult for people to observe traditional artworks for a long time and regularly. For starters, the content of traditional artworks in digital media communication platforms is relatively limited, and second, the audience is primarily comprised of a specific demographic, such as art collectors and beginners in painting. As a result, if basic painting modeling is used as a digital media creative communication form, it will effectively combine the communication advantages of traditional painting foundation and digital media art and will glow with a new artistic charm. People can have a unique cultural experience by combining traditional visual art and modern digital art. Electronic technology products produce works that are faster and more accurate, but the finished products are invariably patterned. The design of works based on traditional painting’s basic modeling can often compensate for the emotional void in digital media art. Designers with a strong painting background typically have extensive professional training and experience. They have extensive design knowledge and understanding, and they will exercise reasonable control over their work during the design process in order to make it more authentic or to create a shock that will reach people’s hearts. Digital media art uses the “unbounded gradient coloring method” in painting to create modern digital technology works and then uses the works for advertising and product sales, among other things, realizing the integration of traditional painting plastic arts and digital media art, and enabling more people to enjoy aesthetic fun and understand the unique emotions of the works.


### 3.2. Model Construction of CNN in Painting Creation

In general, the convolution kernel is initialized at random, with a decimal matrix as the starting point. The algorithm runs during the intermediate training phase, allowing the convolution kernel to learn the weights successfully. Here is an example of iterative updating. Sharing weights has the advantage of reducing connections between network layers and reducing the risk of overfitting. CNN is a type of deep learning that can learn data features automatically and has a high generalization ability. It is widely used in image classification, target detection, and other fields. A convolutional layer, a pooling layer, a fully connected layer, a softmax classification layer, and an activation function make up the CNN, which is a supervised learning method. The feature is extracted through forward propagation in the first stage of the training process. The weights are updated via backpropagation. The purpose of forward propagation is to extract data features, select a sample from the sample set as the current layerl input, then calculate the current layer output through the activation function, and then pass it to the next layer l+1, until the end of the last layer. The current layer output calculation process is as follows:
(1)Yl=fWl+Xl+bl,where lth layer represents the current layer, Xl and Yl represent the current layer input and current layer output, respectively, Wl represents the current layer weight, bl represents the current layer bias, and f represents the activation function of the current layer, this article selects Rectified Linear Units is used as the activation function.The local area of the previous layer’s feature map is connected to the convolution kernel in the forward propagation convolutional layer, and the local features are extracted using the convolution operation. The convolution layer contains multiple convolution kernels. Various convolution kernels extract various features. When performing convolution operations, the same convolution kernel weights are used, but the weights of different convolution kernels differ. The convolution layer is calculated as follows:(2)Xjl=f∑i∈MjXil−1∗kijl+bjl,where lth layer represents the current layer, the l−1th layer represents the previous layer, Xji represents the jth feature map of the current layer, kijl represents the convolution kernel corresponding to the ith feature map of the current layer and the jth feature map of the previous layer., and bjl represents the offset of the jth feature map of the current convolutional layer. The structure of the image retrieval system is shown in Figure 2.Figure 2
Structure diagram of image retrieval system.In the forward propagation pooling layer, the number of input feature maps remains unchanged after the pooling operation. When the pooling step size isn the output feature map size becomes 1/n2 of the input feature map size. The calculation of the pooling layer is as follows:
(3)Xkl=fβkldownXkl−1+bkl,where Xkl and Xkl−1 represent the k-th feature map of the current layer and the previous layer, respectively, down. is the downsampling function, βkl represents the weighting coefficient of the k-th feature map of the current layer, and bkl represents the k-th feature map of the current pooling layer.For a multiclass problem with a sample size ofN and a category size of c, the error square sum loss function is calculated as follows:
(4)EN=12∑n=1N∑k=1ctkn−ykn2,where EN represents the total error of N samples, tknrepresents the k-th dimension label corresponding to the n-th sample, and ykn represents the k-th output predicted in the output corresponding to the n-th sample. The image beauty classification algorithm flow is shown in Figure 3.Figure 3
Flow chart of image aesthetic classification algorithm.


### 3.3. Importance of Painting Creation to Digital Media Design

Painting is a compulsory course for designers, the most basic part of design and a comprehensive subject. It combines the designer’s spirit with the specific material depicted and finally forms a special modeling form. It is different from scientific research and old-fashioned creation. It is produced through the intersection of various disciplines. It is a way to draw pictures by hand. The creativity of designers is the premise and channel of its generation. Painting creation is higher than digital media design because of its uniqueness and flexibility. Only through specific painting can we feel the author’s emotion and the traces left by emotion. With the development of time, the art of painting has been developing. Painting gathers the author’s emotions, forms a vitality stroke, and finally forms a specific picture mode, which is the greatest value of painting creation. It is related to the author’s artistic cultivation and personality characteristics, which is the biggest feature and advantage of painting. This makes painting different from the general digital media design, and digital media cannot have this characteristic. Therefore, an excellent digital eyebrow should have the value of painting works. In order to enable the audience to appreciate the digital media design itself, it is necessary to improve the painting ability of digital media designers.


## 3.1. The Positive Influence of Digital Media Art Design in Painting Creation

Painting art and digital media design are developing together, but their development has different characteristics. Excellent paintings should have vitality and collection value, which cannot be copied. They are not only scientific research but also ornamental. However, digital media is different. Its biggest feature is its reproducibility. It can not only copy indefinitely like printed matter but also make different modifications every time. However, because digital media design is reproducible, the homogenization and plagiarism in its design process are very serious. Painting creation can improve this problem. By adding painting elements to the design process, you can make your works innovative and different. Digital media is developed by relying on information technology. It can make painting more characteristic of the times. It can be said that it has changed a new coat for painting. The flexibility and quickness realized by painting can inject new energy into the artistic brilliance of digital media design. The application of painting creation can make the design of digital media vivid, with unique picture quality. Many children like hand-painted animation more than those produced by digital media. Hand-painted animation integrates the ideas of many authors and is close to children’s psychology. However, although the hand-painted animation is lifelike, it cannot draw the effect of computer digital media. The painting works are strengthened on the basis of computer processing, and various strengthening is carried out according to the design of digital media. By changing the characteristics of strengthening painting for one point and connecting all points in the time period, the painting can be innovated in multiple dimensions. Under the impact of various science and technology in the Internet era, people’s lifetime arrangement began to develop in the direction of fragmentation. The colors of Gerhard Richter’s paintings are based on multiple sources, such as newspapers, books, magazines, and personal photos. Gerhard Richter uses black and white and color photos, or illusory image effects produced by overexposure, underexposure, etc., to form nonemotional, rational, calm, and restrained color picture effects, leading a new aesthetic taste. The development of photographic media technology is an opportunity given to artists by the times. Gerhard Richter uses the picture effects of photographic media to express the artist’s pursuit and challenge of the essence of art, seeking a balance between the development of art itself and the development of society, and he created his paintings with unique colors. The process of digital information acquisition, editing, and processing is shown in Figure1.Figure 1
Process diagram of digital information acquisition, editing, and processing.Digital media technology can provide people with massive information every day, so as to achieve ideal advertising effect. However, the current mode of digital media communication makes it difficult for people to observe traditional artworks for a long time and regularly. For starters, the content of traditional artworks in digital media communication platforms is relatively limited, and second, the audience is primarily comprised of a specific demographic, such as art collectors and beginners in painting. As a result, if basic painting modeling is used as a digital media creative communication form, it will effectively combine the communication advantages of traditional painting foundation and digital media art and will glow with a new artistic charm. People can have a unique cultural experience by combining traditional visual art and modern digital art. Electronic technology products produce works that are faster and more accurate, but the finished products are invariably patterned. The design of works based on traditional painting’s basic modeling can often compensate for the emotional void in digital media art. Designers with a strong painting background typically have extensive professional training and experience. They have extensive design knowledge and understanding, and they will exercise reasonable control over their work during the design process in order to make it more authentic or to create a shock that will reach people’s hearts. Digital media art uses the “unbounded gradient coloring method” in painting to create modern digital technology works and then uses the works for advertising and product sales, among other things, realizing the integration of traditional painting plastic arts and digital media art, and enabling more people to enjoy aesthetic fun and understand the unique emotions of the works.


## 3.2. Model Construction of CNN in Painting Creation

In general, the convolution kernel is initialized at random, with a decimal matrix as the starting point. The algorithm runs during the intermediate training phase, allowing the convolution kernel to learn the weights successfully. Here is an example of iterative updating. Sharing weights has the advantage of reducing connections between network layers and reducing the risk of overfitting. CNN is a type of deep learning that can learn data features automatically and has a high generalization ability. It is widely used in image classification, target detection, and other fields. A convolutional layer, a pooling layer, a fully connected layer, a softmax classification layer, and an activation function make up the CNN, which is a supervised learning method. The feature is extracted through forward propagation in the first stage of the training process. The weights are updated via backpropagation. The purpose of forward propagation is to extract data features, select a sample from the sample set as the current layerl input, then calculate the current layer output through the activation function, and then pass it to the next layer l+1, until the end of the last layer. The current layer output calculation process is as follows:
(1)Yl=fWl+Xl+bl,where lth layer represents the current layer, Xl and Yl represent the current layer input and current layer output, respectively, Wl represents the current layer weight, bl represents the current layer bias, and f represents the activation function of the current layer, this article selects Rectified Linear Units is used as the activation function.The local area of the previous layer’s feature map is connected to the convolution kernel in the forward propagation convolutional layer, and the local features are extracted using the convolution operation. The convolution layer contains multiple convolution kernels. Various convolution kernels extract various features. When performing convolution operations, the same convolution kernel weights are used, but the weights of different convolution kernels differ. The convolution layer is calculated as follows:(2)Xjl=f∑i∈MjXil−1∗kijl+bjl,where lth layer represents the current layer, the l−1th layer represents the previous layer, Xji represents the jth feature map of the current layer, kijl represents the convolution kernel corresponding to the ith feature map of the current layer and the jth feature map of the previous layer., and bjl represents the offset of the jth feature map of the current convolutional layer. The structure of the image retrieval system is shown in Figure 2.Figure 2
Structure diagram of image retrieval system.In the forward propagation pooling layer, the number of input feature maps remains unchanged after the pooling operation. When the pooling step size isn the output feature map size becomes 1/n2 of the input feature map size. The calculation of the pooling layer is as follows:
(3)Xkl=fβkldownXkl−1+bkl,where Xkl and Xkl−1 represent the k-th feature map of the current layer and the previous layer, respectively, down. is the downsampling function, βkl represents the weighting coefficient of the k-th feature map of the current layer, and bkl represents the k-th feature map of the current pooling layer.For a multiclass problem with a sample size ofN and a category size of c, the error square sum loss function is calculated as follows:
(4)EN=12∑n=1N∑k=1ctkn−ykn2,where EN represents the total error of N samples, tknrepresents the k-th dimension label corresponding to the n-th sample, and ykn represents the k-th output predicted in the output corresponding to the n-th sample. The image beauty classification algorithm flow is shown in Figure 3.Figure 3
Flow chart of image aesthetic classification algorithm.


## 3.3. Importance of Painting Creation to Digital Media Design

Painting is a compulsory course for designers, the most basic part of design and a comprehensive subject. It combines the designer’s spirit with the specific material depicted and finally forms a special modeling form. It is different from scientific research and old-fashioned creation. It is produced through the intersection of various disciplines. It is a way to draw pictures by hand. The creativity of designers is the premise and channel of its generation. Painting creation is higher than digital media design because of its uniqueness and flexibility. Only through specific painting can we feel the author’s emotion and the traces left by emotion. With the development of time, the art of painting has been developing. Painting gathers the author’s emotions, forms a vitality stroke, and finally forms a specific picture mode, which is the greatest value of painting creation. It is related to the author’s artistic cultivation and personality characteristics, which is the biggest feature and advantage of painting. This makes painting different from the general digital media design, and digital media cannot have this characteristic. Therefore, an excellent digital eyebrow should have the value of painting works. In order to enable the audience to appreciate the digital media design itself, it is necessary to improve the painting ability of digital media designers.


## 4. Result Analysis and Discussion

Because small sample data sets were used, the number of convolutional layers in the face image aesthetic classification network model was determined by adding 6 layers, 7 layers, and 8 layers in order, based on the number of layers in the LeNet-5 network. Each convolutional layer includes a pooling operation, and the convolutional layer performs network model training. The test set images are classified and recognized after the network model training is completed. The accuracy rate does not show a clear upward trend as the number of convolutional layers increases during model training. As a result, choose the T2 network structure that corresponds to the first peak point of the accuracy rate on the basis of network simplicity. The test result of the seven-layer network structure is shown in Figure4.Figure 4
Training diagram of seven-layer network structure.The meaning of each curve in the following similar pictures is the same. Obviously, the higher the accuracy value and the lower the loss value, the better the trained neural network. In the research process, we focus on the yellow curve—hereinafter referred to as the accuracy value and the green curve—hereinafter referred to as the loss value. Observing the experimental results in Figure4, it is found that the accuracy value of the seven-layer network is high and the loss value is low. Therefore, the training and verification of the seven-layer network have achieved good experimental results. The experimental results of eight layer network structure are shown in Figure 5.Figure 5
Training diagram of eight-layer network structure.Experiments show that the accuracy value of the eight-layer network is slightly lower than that of the seven-layer network and slightly higher than that of the seven-layer network. So obviously, the training result of the eight-layer network is not as good as that of the seven-layer network. This is because although the eight-layer network can extract richer features by increasing the number of layers and convolution kernels, it is difficult to achieve better results due to the limited size of the input pictures. On the contrary, because there are many feature maps, but it is difficult to express the image features more accurately, the loss value will increase. The experimental results of the six-layer network structure are shown in Figure6.Figure 6
Six-layer network structure training diagram.Figure6 shows that the training results of the six-layer and seven-layer networks are similar, as is the loss, but the accuracy value is still low. This demonstrates that in terms of image feature expression, the six-layer network is still inferior to the seven-layer network. ReLU was chosen as the activation function in the experiment. Other parameters remain constant throughout the experiment. The accuracy (val) in the upper yellow curve represents the accuracy in the verification process. The loss (val), which can be expressed as the loss value in the verification process, is represented by the middle green curve; the loss (train), which represents the loss value in the training process, is represented by the blue curve at the bottom. The upper yellow curve in Figure 4 represents the accuracy (val), which indicates the accuracy of the verification process. The loss (val), which can be expressed as the loss value in the verification process, is represented by the middle green curve. The blue curve at the bottom represents loss (train), which represents the training process’s loss value.The results show that the change of the number of features in each layer of the full connection layer will affect the accuracy of the classification results. At the same time, with the increase of the full connection layer and with the decrease of feature elements, the classification accuracy will also be affected. Therefore, the first peak point of the experimental results is selected. The test process is shown in Figure7.Figure 7
Network training results with 12-24 convolution kernels.The experimental results can be explained. By increasing the structural width of CNN, the network can adapt to feature extraction of various sizes and realize the fusion of various features, thus enhancing the feature learning ability of CNN. Especially when the spatial resolution of the input image is large, the features of the input image are richer and more comprehensive, and increasing the network width is more conducive to effectively extracting various size features. Therefore, the increase of the structural width of CNN is beneficial to the improvement of classification accuracy of CNN model. The test process is shown in Figure8.Figure 8
Network training results with 24-48 convolution kernels.As the number of training samples increases, the powerful feature learning ability of the CNN can be more reflected, and the learned features can effectively express the data, which is conducive to the improvement of classification accuracy. The test process is shown in Figure9.Figure 9
Network training results with 30-60 convolution kernels.Despite the fact that the deeper the level, the more complex it is, CNN is built on large data sets and large-scale parallel computing. The so-called adjusting measures to local conditions necessitates conducting scientific research in conjunction with the actual situation, and the experimental environment of this paper is a typical experimental environment with average computing power and research on small data sets. As a result, rather than pursuing the unrealistic deep network, we should choose a CNN structure that is appropriate for the actual environment. The goal of this experiment is to choose a better CNN structure under real-world conditions in order to achieve the goal of future system design. The final search results will be verified in the system design part at the same time. The test process is shown in Figure10.Figure 10
Network training results with 40-80 convolution kernels.The convolution neural network structure suitable for this article’s artwork image data set is selected through experiments. Observing the above experimental results, it is not difficult to find that there is not much difference among the four groups of experimental results, and all of them have achieved good training results. Secondly, the combination of 40-80 convolution kernels in two convolution layers has better effect: this is because the increase of convolution kernels will extract richer image features, but because of the small data set and the small image size, increasing the convolution kernels again will not help the improvement of the experimental effect but will reduce the experimental effect.With the rapid rise and steady development of digital media art, many people believe that the traditional basic modeling technology of painting will be replaced. Some teaching and research scholars in art colleges have also said that most of their energy should be focused on the study and mastery of digital media technology. However, this statement ignores the complementary relationship between the basic modeling of painting and digital media art. The basic modeling of painting has very important theoretical and practical significance in the design of digital media art. Painting basic modeling technology is the designer’s grasp and application of design knowledge such as design theory and design methodology in digital media art creation. It is also the soul and thought of the designer in the design of modern artworks. What kind of digital media technology designers choose to apply is often determined by the basic modeling design concept of painting. Good art is mostly realized by designers with solid basic modeling skills of painting with the help of digital media platform. Digital media art adds the vitality of the basic modeling language of painting. It makes the static language picture move vividly. Even through digital media technology, it can create a virtual world for us and let us feel a new visual world in the virtual world. In fact, the application of painting basic modeling is often the source of digital media art creation. Virtual reality may seem to have nothing to do with history, but its realization has a lot to do with traditional painting art. The most obvious expression lies in the discontinuous activities of exploring illusory images. The profound influence of painting basic modeling on digital media art mainly lies in the expression of ideological design, that is, endowing the work with depth and connotation, rather than just staying on the surface techniques. It makes modern artworks reflect diversity and higher originality, and it is also works with more appeal and affinity.


## 5. Conclusions

Digital media technology has created a new medium for the dissemination of painting art. Through digital collection, digital editing, and digital communication, the artistic creation of painting has achieved the popularization of paper media with the help of digital media. Deep learning has emerged as a hotspot for research in the field of machine learning, and it is now widely used in a variety of fields. In this paper, we propose a method for extracting features from painting images using CNN and achieving painting image classification. Simultaneously, a new convolutional layer is connected in parallel between two convolutional layers based on the traditional serial CNN structure to widen the network structure, enable multisize feature extraction and multifeature fusion, and thus improve CNN’s feature learning ability. The impact of the number of training samples on CNN classification performance is also investigated. It has been discovered that increasing the number of training samples improves the classification performance of networks by reflecting the feature learning ability of CNNs. The features extracted by traditional image classification methods and deep learning methods can be combined in the future to improve the algorithm’s classification performance, based on the findings of this paper. Many disciplines collide in the creation of art. The most intelligent place for designers to integrate skills with art is to integrate computer knowledge with painting quality, not only to learn computer knowledge but also to improve painting quality. Only in this manner will we be able to create appealing works.



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*Source: 9519274-2022-03-18.xml*