# Analysis of the Effects of Arts and Crafts in Public Mental Health Education Based on Artificial Intelligence Technology

Authors: Linchong Ji; Zhiyong Liu
Journal: Journal of Environmental and Public Health
 (2022)

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## Abstract

Arts and crafts, with their very different styles due to many factors such as times, regions, technologies, and cultures and nationalities, have undergone an extremely long process, and it is only through continuous superimposition, development, and innovation that they have gradually formed the posture of today’s arts and crafts. Public mental health education is the main way to promote the psychological health development of the public in colleges and universities at present. And among them, sound personality and good self-awareness is one of the important standards of psychological health of the large public and one of the important tasks of mental health education. As an effective psychological test and treatment method, arts and crafts analysis are an important part of mental health education. It has a certain role in improving the level of self-awareness and promoting the integration of personality. Art and craft analysis has advantages in mental health and educational group counseling that cannot be replaced by other words and activities, so it can be used in mental health education courses. It can be used in teaching self-awareness. In order to combine the development of arts and crafts with the development concept and promotion ideas of public mental health education, this article proposes an analysis of the role of arts and crafts in public mental health education based on artificial intelligence computing to enhance the development of arts and crafts from a new perspective and seek the inheritance and innovation of arts and crafts and public mental health education in the new historical period, and proves the proposed method in the relevant dataset. The validity of the proposed method is demonstrated in the relevant dataset.


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## Body

## 1. Introduction

Arts and crafts are a unique art discipline that has a long history in China and has been explored and developed over a long period of time to become a relatively mature and complete discipline in the art field. Art and innovation have always been complementary to each other, and it is the continuous innovation and optimization from simple to complex and from rough to fine that has led to the development of arts and crafts design, which has evolved into an important factor affecting the daily production and life of human beings. In the case of arts and crafts design, its development process is the process of innovation [1–3]. Only by keeping pace with the times and synchronizing human aesthetic concepts can arts and crafts design achieve sustainable development.First, innovation in arts and crafts design is conducive to strengthening the infectious power of artworks. In the design of art and craft works, designers should not stick to the rules and design according to the corresponding templates. Innovative arts and crafts design should be made to strengthen the artistic charm and infectious power of arts and crafts works, to obtain the favor of most audience groups [4–7]. Secondly, the innovation of arts and crafts design is conducive to meeting people’s spiritual needs. At present, arts and crafts works are popular among people, which makes designers deeply feel and realize the huge potential of arts and crafts design work, prompting them to complete the design work independently and actively. However, this change in thinking has also directly increased the pressure of designers’ work, and people’s requirements for arts and crafts design have gradually begun to change towards deep spiritual needs. In this regard, designers must target innovation and improvement, to meet people’s spiritual needs to the greatest extent. Again, the innovation of arts and crafts design is conducive to optimizing the characteristics of artworks. In the process of arts and crafts design, the introduction of innovative thinking can promote designers to analyze the value of arts and crafts works based on different perspectives and levels of deep consideration, to ensure that arts and crafts works can have both aesthetic and functional characteristics. The role of arts and crafts in public mental health education is shown in Figure 1.Figure 1
The role of arts and crafts in public mental health education.Art education can cultivate people with a sense of beauty, allowing them to see beauty in the most ordinary things; it also enables them to know how to use ordinary things around them to create beauty, giving them a positive and happy attitude towards life, and it enables them to face the hardships of life with a sense of beauty. As an important component of art education, arts and crafts is precisely the purpose of self-healing and nurturing the healthy growth of the mind through the transfer and transformation of creativity and aesthetic experience by using the perception of beauty in its unique nonverbal expression [8–10]. Therefore, we should actively explore the psychological healing function of arts and crafts, that is, the healthcare of arts and crafts for the healthy psychology of the subject. The origin of art therapy can be traced back to prehistoric times, when humans felt fear and panic about many unknown phenomena such as nature and man himself, so they left many murals in caves to express their awe to relieve their inner pressure.Today, modern medical psychologists have shown through their research that art has a significant therapeutic and healthcare effect in regulating psychological anxiety and emotional disorders in modern people. They believe that art and its educational activities are a kind of panacea for maintaining and improving physical and mental health, and a kind of nonverbal psychotherapy that is quite effective. Through arts and crafts, calligraphy, seal engraving, sculpture, architecture, etc., art and its educational activities explore, express, and create beauty, so that the subject can feel beauty, appreciate beauty and love beauty in the process of education, cultivate beautiful ideas, and use the psychological suggestion of beautiful ideas to create a lively and pleasant spiritual realm, so that life is full of health and vitality, and the mind is calm and peaceful [11–14]. The suggestiveness and creativity of this good intention can fully mobilize the psychological potential of individuals, so that their physiological functions show a good emotional reflection, the cerebral cortex, and the central nervous system to produce a positive stimulation and promote a more pleasant and strong body and mind.As a special group of young people loaded with high expectations from family and society, the contemporary public is facing more opportunities and at the same time is under greater psychological pressure and challenges. In this sense, the public is a high-risk group for mental health problems. According to a national sample survey, 23% of the public has different degrees of mental health disorders or psychological abnormalities. Growing adults are more likely to experience more anxiety and frustration due to their unstable state of mental activity, incomplete cognitive structure, lack of synchronization between physiological and psychological maturity, and lack of identification with society and family, and are therefore more likely to have psychological problems. If temporary psychological barriers are not eliminated in time, they will produce adverse reactions and affect the healthy development of the psyche in the future and may even lead to psychological disorders that are difficult to save in the future [15].From the development of arts and crafts in recent years, the use of artificial intelligence in the field of arts and crafts has gradually become widespread. Especially in the process of arts and crafts design education and teaching, the use of artificial intelligence helps to improve the aesthetic level and improve the quality of teaching. Arts and crafts built on artificial intelligence can show static knowledge in a dynamic mode, helping the public to understand art- and design-related knowledge more intuitively. In addition, arts and crafts design has humanistic and artistic characteristics, and the effective combination of big data, AI technology, and VR technology in the age of artificial intelligence can promote the cultivation of public aesthetic consciousness, spread public thinking, and guide them to establish correct mental health concepts. Therefore, under the era of artificial intelligence, arts and crafts design should change its own concept, integrate various technologies and advantages involved in artificial intelligence into the process of arts and crafts design, optimize teaching resources, innovate teaching mode, create a good artistic atmosphere for arts and crafts, guide the public to feel and experience the beauty of art and design, and realize the innovation and reform of art and design teaching in colleges and universities [16].The main contributions of this article are as follows. Firstly, it analyzes that in arts and crafts design, the role of arts and crafts in healthcare for public mental health should be actively explored, and through effective aesthetic penetration and aesthetic deepening, education, and self-education that integrates knowledge, emotion, intention, and action should be implemented, and rich arts and crafts activities should be carried out to cultivate healthy aesthetic consciousness, aesthetic emotion, and aesthetic behavior of the subject to sound psychology and develop personality. This article proposes a model for analyzing the role of arts and crafts in public mental health education by artificial intelligence technology. The neural network model with deep learning can analyze arts and crafts for public mental health education quickly and accurately. The experiments demonstrate the effectiveness of the proposed method and provide a feasible solution for the analysis of the role of arts and crafts in public mental health education in batch and fast.


## 2. Related Work

### 2.1. Arts and Crafts Design

The arts and crafts industry, with its profound cultural heritage and exquisite craftsmanship, has opened a new research direction and creative field for the current cultural and creative industries. With a far-sighted view on the inheritance and innovation of traditional arts and crafts, we bring life and vitality to the development of arts and crafts by using classics as inheritance and innovation. How to make traditional arts and crafts rejuvenate is a topic that has been explored in the past few years. Designing arts and crafts that meet the needs of contemporary people, meet the aesthetic value of the public, and have practical functions has become a necessary condition for the transformation and upgrading of arts and crafts. The innovation of arts and crafts needs several aspects to complement each other, such as the improvement of the environment for the development of arts and crafts, the change of design concept, the cultivation of the innovative spirit of arts and crafts inheritors, the integration of arts and crafts with science and technology, and the cross-border cooperation of arts and crafts [17]. These changes are urgent, and only the collision of innovative thinking can give a new vitality, so that our life can be splendid because of innovation, and arts and crafts can be inherited because of innovation.The improvement of the environment for the development of arts and crafts is the external condition for the survival of arts and crafts, as the social environment and economic environment interpenetrate each other. If we can change our perspective and our existing habitual way of thinking, we may be able to open new horizons. Unlike pure art, which is confined to the upper class and the literati, arts and crafts are directly related to the society and the people and are closely related to the social environment. A prosperous socioeconomic environment is naturally conducive to the development of arts and crafts and provides a material basis for their development. Only arts and crafts that penetrate deeply into people’s lives can develop harmoniously in the social environment, so that their artistic personality can gain vitality and win people’s consensus [18–21].Nowadays, Chinese elements have become a hot spot of attention on the world stage, and Chinese arts and crafts, with their profound cultural heritage, rich historical connotation, and regional characteristics, are favored by people from all over the world, and arts and crafts as one of the elements play their due function of cultural dissemination. The last decade has been a golden decade for the development of the arts and crafts industry. Consumers’ demand for arts and crafts products has been diversified and multileveled, thus driving the rapid development of the industry and providing a wide space for China’s arts and crafts industry to achieve higher economic and cultural values. No matter what industry the designer belongs to, deep cultural cultivation is necessary, and the designer’s taste directly affects his works. It is important for designers to open their eyes, such as traveling abroad, attending industry exhibitions, and using the Internet are useful ways [22,23]. An excellent designer should learn a wide range of knowledge, consciously accumulate knowledge, and develop their own ability to feel the beauty, so that they can be in touch with it when designing.A designer is different from a craftsman or an artist. The creation of an artist is to a large extent a personal act, and he can create works expressing his own ideas at will. The designer’s creation is a social act, holding the concept of human-oriented design and putting the needs of consumers in the first place, and his works are accepted by the public in daily life after forming products. In addition, the designer should also have sufficient understanding of the whole process of the product from design and manufacturing to the market. At present, corporate designers are subject to greater constraints because companies pursue economic benefits first and personalization later.


### 2.2. Public Mental Health Education

Clinical psychology research shows that emotions dominate health. Maintaining good moods and stable emotions is one of the most helpful forces for physical and mental health in the human body. As an important part of arts and crafts education, the emotional factor is always present throughout the aesthetic and creative activities, and it can be said that it is difficult to produce true beauty without emotion. Healthy and noble emotions can balance many aspects of an individual’s mental activity, enabling them to treat all kinds of pressure with a calm and friendly attitude, express their emotions reasonably, and gain inner peace and positive motivation for life [24–26].Nourishing the heart for beauty. Perceiving and appreciating beauty is the foundation and key to aesthetic education, the core of aesthetic education. Giving up this, emotions will be indifferent to any thing of beauty. The ingestion of profound aesthetic feelings requires going deep into the object of beauty or the environment it is in to observe, experience, and comprehend, and therefore, often lead the public into nature, or observation, or description, or writing, or collage, or imagination, “to the body of,” first-hand experience and then taste, guide its aesthetic mind to insight into nature, to feel the spirit of nature, touch the truth of nature, perceive the beauty of all life, think about the ugliness and evil in the world, and nourish the heart of beauty, to moisten the love of beauty. Arts and crafts education is a kind of emotional education, which mobilizes various psychological functions of the subject through beautiful things and sublimates emotions, so that through rich inner experience, it is psychologically moved, emotionally resonant, and temperamentally cultivated.When many people look at a picture, if it is an ink-painted landscape picture, they will have a sense of magnificence in their hearts; if it is a gold-blue flower and butterfly picture, they will have a sense of beauty in their hearts. Therefore, if a landscape picture is hung in a hall, people in the hall will feel more solemn and respectful; if a flower and bird picture is hung in a room, people in the room will feel happier. Plato also thought that we should look for some competent artists to portray the beautiful aspects of nature, so that our young people, like living in a windy and warm area, where everything around them is good for their health, will be exposed to beautiful works every day, like breathing a breeze from a secluded realm, to breathe their good influence, so that they will unconsciously cultivate from childhood. For the love of beauty, cultivate the habit of integrating beauty in the mind. Excellent arts and crafts work can inspire the viewer’s empathy through the sensual and changing forms of artistic composition, and get infected from them, thus generating a longing and aspiration for beauty and spurning and despising ugliness, and gradually entering the realm of truth, goodness, and beauty. This sublimation of emotion can make people feel beautiful things freely and happily under the requirement of inner desire, accept the baptism of beauty, and produce the love of beauty [25].Enriching activities for a healthy personality. One of the most important tasks of upbringing is to make people governed by forms even in their purely natural state of life. The moral man can only develop from the aesthetic man and cannot arise from the state of nature. Immortal artistic creation reflects the author’s conception of art, beauty, and love for life. The process of appreciating and creating beautiful things often sublimates human emotions, and this sublimation will play a subtle role in regulating human behavior and prompting it to become a moral being.Therefore, in arts and crafts education, we should make good use of all the resources of beauty, organically penetrate the inner world and real life of people, form a conscious rational force, and strengthen aesthetic experience and aesthetic training through various forms of arts and crafts activities, so as to shape the healthy and complete personality of the subject. Practice the eye for beauty. Beauty is everywhere, and for our eyes, it is not a lack of beauty, but a lack of discovery. But a pair of eyes good at finding beauty needs to be honed for a long time. Only when the visual object causes physiological and psychological pleasure does perception become associated with aesthetics. Arts and crafts education is to train the perceiving subject to select different perspectives in specific aesthetic activities, to use symmetry, balance, rhythm, rhyme, and other laws of beauty to observe and analyze objects, to raise daily perception to aesthetic perception, and to develop a pair of “aesthetic eyes,” that is, from unconscious viewing of nature to a conscious, active, and selective observation. The “aesthetic eye” to observe things can fully mobilize the subject’s imagination, association, emotion, and other psychological factors, and consciously use the laws of beauty, to test and feel the beauty of things, so as to grasp the natural objects of the original.


### 2.3. Artificial Intelligence Technology

The technology of recognizing the style and mental health state of arts and crafts probably emerged at the end of the 20th century, mainly through the technology of image texture generation to realize the migration of style. Research on image texture requires researchers to build models manually, the core idea of which is to generate texture through statistics of local features of images, without which models cannot be built at all, and a model can only do one style or scene [26–28]. In addition, the computer computing power was not strong at that time, so the development of arts and crafts style and mental health state recognition technology was very slow. The predecessor of convolutional neural network is the visual cortical map created by Hubel and Wiesel by recording the brain feedback formed by the stimulation of a cat in a specific mode. leNet-5 formed the prototype of contemporary convolutional neural network, and based on the LeNet-5 model, the convolutional neural network has a more systematic definition and precise structure under the research of researchers. Convolutional neural networks are a class of feedforward neural networks with deep structure and convolutional computation built after biological visual perception mechanism, which are widely used in image recognition, behavioral cognition, pose estimation, and natural language processing because of their ability to learn data stably.Convolutional neural network-based art and craft style and mental health state recognition technology art and craft style migration is a special application of convolutional neural network (CNN) in the field of computer vision, fully demonstrating the ability of convolutional neural network representation learning, able to learn features and learn the process of extracting features to avoid the trouble of manual extraction of features. It consists of input layer, convolutional layer, excitation function, pooling layer, and fully connected layer. The convolutional layer is an important part of the CNN and is used to extract feature values. Different convolutional kernels can extract different features [29, 30]. The lower convolutional layers can only extract low-level features such as edges, lines, and corners, while the higher layers can use the lower features to obtain more complex features. The pooling layer is a down-sampling operation after feature extraction by the convolution kernel, which is mainly used to perform feature dimensionality reduction and improve computational speed by compressing the number of data and parameters, and can control overfitting and improve the robustness of the network. Based on the published structural model, the algorithm is optimized for both style and content by introducing the target content image based on texture synthesis and modifying the loss function to combine the style of any one image content to form an image with artistic style characteristics.The visual processing is carried out by training a multilayer convolutional neural network so that the computer discriminates and learns the artistic style. However, it is obvious from the generated images that some of the image contents are distorted, details are lost, and the time consumption of the trained convolutional network is long, and the degree of migration cannot be controlled. Subsequently, the control of details in the migration of the Arts and crafts style was enhanced, but there was still no control over the image content. After that, the Fast Neural Style approach improved the drawback of the long training time of the original craft style migration, and the GPU usually only needs to run for a few seconds to generate the corresponding craft style migration results after each style model is trained, but the generated image effect is still not improved. With the continuous development of technology, the technology of arts and crafts style and mental health status recognition is becoming more and more mature, but the problems of image distortion and loss of details still exist, and the main breakthrough point of the future technology of arts and crafts style and mental health status recognition based on convolutional neural network is to get the synthetic image with the best matching degree and lower loss.


## 2.1. Arts and Crafts Design

The arts and crafts industry, with its profound cultural heritage and exquisite craftsmanship, has opened a new research direction and creative field for the current cultural and creative industries. With a far-sighted view on the inheritance and innovation of traditional arts and crafts, we bring life and vitality to the development of arts and crafts by using classics as inheritance and innovation. How to make traditional arts and crafts rejuvenate is a topic that has been explored in the past few years. Designing arts and crafts that meet the needs of contemporary people, meet the aesthetic value of the public, and have practical functions has become a necessary condition for the transformation and upgrading of arts and crafts. The innovation of arts and crafts needs several aspects to complement each other, such as the improvement of the environment for the development of arts and crafts, the change of design concept, the cultivation of the innovative spirit of arts and crafts inheritors, the integration of arts and crafts with science and technology, and the cross-border cooperation of arts and crafts [17]. These changes are urgent, and only the collision of innovative thinking can give a new vitality, so that our life can be splendid because of innovation, and arts and crafts can be inherited because of innovation.The improvement of the environment for the development of arts and crafts is the external condition for the survival of arts and crafts, as the social environment and economic environment interpenetrate each other. If we can change our perspective and our existing habitual way of thinking, we may be able to open new horizons. Unlike pure art, which is confined to the upper class and the literati, arts and crafts are directly related to the society and the people and are closely related to the social environment. A prosperous socioeconomic environment is naturally conducive to the development of arts and crafts and provides a material basis for their development. Only arts and crafts that penetrate deeply into people’s lives can develop harmoniously in the social environment, so that their artistic personality can gain vitality and win people’s consensus [18–21].Nowadays, Chinese elements have become a hot spot of attention on the world stage, and Chinese arts and crafts, with their profound cultural heritage, rich historical connotation, and regional characteristics, are favored by people from all over the world, and arts and crafts as one of the elements play their due function of cultural dissemination. The last decade has been a golden decade for the development of the arts and crafts industry. Consumers’ demand for arts and crafts products has been diversified and multileveled, thus driving the rapid development of the industry and providing a wide space for China’s arts and crafts industry to achieve higher economic and cultural values. No matter what industry the designer belongs to, deep cultural cultivation is necessary, and the designer’s taste directly affects his works. It is important for designers to open their eyes, such as traveling abroad, attending industry exhibitions, and using the Internet are useful ways [22,23]. An excellent designer should learn a wide range of knowledge, consciously accumulate knowledge, and develop their own ability to feel the beauty, so that they can be in touch with it when designing.A designer is different from a craftsman or an artist. The creation of an artist is to a large extent a personal act, and he can create works expressing his own ideas at will. The designer’s creation is a social act, holding the concept of human-oriented design and putting the needs of consumers in the first place, and his works are accepted by the public in daily life after forming products. In addition, the designer should also have sufficient understanding of the whole process of the product from design and manufacturing to the market. At present, corporate designers are subject to greater constraints because companies pursue economic benefits first and personalization later.


## 2.2. Public Mental Health Education

Clinical psychology research shows that emotions dominate health. Maintaining good moods and stable emotions is one of the most helpful forces for physical and mental health in the human body. As an important part of arts and crafts education, the emotional factor is always present throughout the aesthetic and creative activities, and it can be said that it is difficult to produce true beauty without emotion. Healthy and noble emotions can balance many aspects of an individual’s mental activity, enabling them to treat all kinds of pressure with a calm and friendly attitude, express their emotions reasonably, and gain inner peace and positive motivation for life [24–26].Nourishing the heart for beauty. Perceiving and appreciating beauty is the foundation and key to aesthetic education, the core of aesthetic education. Giving up this, emotions will be indifferent to any thing of beauty. The ingestion of profound aesthetic feelings requires going deep into the object of beauty or the environment it is in to observe, experience, and comprehend, and therefore, often lead the public into nature, or observation, or description, or writing, or collage, or imagination, “to the body of,” first-hand experience and then taste, guide its aesthetic mind to insight into nature, to feel the spirit of nature, touch the truth of nature, perceive the beauty of all life, think about the ugliness and evil in the world, and nourish the heart of beauty, to moisten the love of beauty. Arts and crafts education is a kind of emotional education, which mobilizes various psychological functions of the subject through beautiful things and sublimates emotions, so that through rich inner experience, it is psychologically moved, emotionally resonant, and temperamentally cultivated.When many people look at a picture, if it is an ink-painted landscape picture, they will have a sense of magnificence in their hearts; if it is a gold-blue flower and butterfly picture, they will have a sense of beauty in their hearts. Therefore, if a landscape picture is hung in a hall, people in the hall will feel more solemn and respectful; if a flower and bird picture is hung in a room, people in the room will feel happier. Plato also thought that we should look for some competent artists to portray the beautiful aspects of nature, so that our young people, like living in a windy and warm area, where everything around them is good for their health, will be exposed to beautiful works every day, like breathing a breeze from a secluded realm, to breathe their good influence, so that they will unconsciously cultivate from childhood. For the love of beauty, cultivate the habit of integrating beauty in the mind. Excellent arts and crafts work can inspire the viewer’s empathy through the sensual and changing forms of artistic composition, and get infected from them, thus generating a longing and aspiration for beauty and spurning and despising ugliness, and gradually entering the realm of truth, goodness, and beauty. This sublimation of emotion can make people feel beautiful things freely and happily under the requirement of inner desire, accept the baptism of beauty, and produce the love of beauty [25].Enriching activities for a healthy personality. One of the most important tasks of upbringing is to make people governed by forms even in their purely natural state of life. The moral man can only develop from the aesthetic man and cannot arise from the state of nature. Immortal artistic creation reflects the author’s conception of art, beauty, and love for life. The process of appreciating and creating beautiful things often sublimates human emotions, and this sublimation will play a subtle role in regulating human behavior and prompting it to become a moral being.Therefore, in arts and crafts education, we should make good use of all the resources of beauty, organically penetrate the inner world and real life of people, form a conscious rational force, and strengthen aesthetic experience and aesthetic training through various forms of arts and crafts activities, so as to shape the healthy and complete personality of the subject. Practice the eye for beauty. Beauty is everywhere, and for our eyes, it is not a lack of beauty, but a lack of discovery. But a pair of eyes good at finding beauty needs to be honed for a long time. Only when the visual object causes physiological and psychological pleasure does perception become associated with aesthetics. Arts and crafts education is to train the perceiving subject to select different perspectives in specific aesthetic activities, to use symmetry, balance, rhythm, rhyme, and other laws of beauty to observe and analyze objects, to raise daily perception to aesthetic perception, and to develop a pair of “aesthetic eyes,” that is, from unconscious viewing of nature to a conscious, active, and selective observation. The “aesthetic eye” to observe things can fully mobilize the subject’s imagination, association, emotion, and other psychological factors, and consciously use the laws of beauty, to test and feel the beauty of things, so as to grasp the natural objects of the original.


## 2.3. Artificial Intelligence Technology

The technology of recognizing the style and mental health state of arts and crafts probably emerged at the end of the 20th century, mainly through the technology of image texture generation to realize the migration of style. Research on image texture requires researchers to build models manually, the core idea of which is to generate texture through statistics of local features of images, without which models cannot be built at all, and a model can only do one style or scene [26–28]. In addition, the computer computing power was not strong at that time, so the development of arts and crafts style and mental health state recognition technology was very slow. The predecessor of convolutional neural network is the visual cortical map created by Hubel and Wiesel by recording the brain feedback formed by the stimulation of a cat in a specific mode. leNet-5 formed the prototype of contemporary convolutional neural network, and based on the LeNet-5 model, the convolutional neural network has a more systematic definition and precise structure under the research of researchers. Convolutional neural networks are a class of feedforward neural networks with deep structure and convolutional computation built after biological visual perception mechanism, which are widely used in image recognition, behavioral cognition, pose estimation, and natural language processing because of their ability to learn data stably.Convolutional neural network-based art and craft style and mental health state recognition technology art and craft style migration is a special application of convolutional neural network (CNN) in the field of computer vision, fully demonstrating the ability of convolutional neural network representation learning, able to learn features and learn the process of extracting features to avoid the trouble of manual extraction of features. It consists of input layer, convolutional layer, excitation function, pooling layer, and fully connected layer. The convolutional layer is an important part of the CNN and is used to extract feature values. Different convolutional kernels can extract different features [29, 30]. The lower convolutional layers can only extract low-level features such as edges, lines, and corners, while the higher layers can use the lower features to obtain more complex features. The pooling layer is a down-sampling operation after feature extraction by the convolution kernel, which is mainly used to perform feature dimensionality reduction and improve computational speed by compressing the number of data and parameters, and can control overfitting and improve the robustness of the network. Based on the published structural model, the algorithm is optimized for both style and content by introducing the target content image based on texture synthesis and modifying the loss function to combine the style of any one image content to form an image with artistic style characteristics.The visual processing is carried out by training a multilayer convolutional neural network so that the computer discriminates and learns the artistic style. However, it is obvious from the generated images that some of the image contents are distorted, details are lost, and the time consumption of the trained convolutional network is long, and the degree of migration cannot be controlled. Subsequently, the control of details in the migration of the Arts and crafts style was enhanced, but there was still no control over the image content. After that, the Fast Neural Style approach improved the drawback of the long training time of the original craft style migration, and the GPU usually only needs to run for a few seconds to generate the corresponding craft style migration results after each style model is trained, but the generated image effect is still not improved. With the continuous development of technology, the technology of arts and crafts style and mental health status recognition is becoming more and more mature, but the problems of image distortion and loss of details still exist, and the main breakthrough point of the future technology of arts and crafts style and mental health status recognition based on convolutional neural network is to get the synthetic image with the best matching degree and lower loss.


## 3. Methods

### 3.1. Model Architecture

The region-difference stylization model in this article is shown in Figure2. After the content image passes through the DeepLabV3 semantic segmentation network, a segmentation map with n semantic regions is generated, and different styles are adopted for stylization in each of these n regions. Based on the neural style conversion algorithm, a pretrained VGG-16 neural network is used to calculate the style loss and content loss to ensure the stylization effect while reducing the computational burden. In the loss function part, the content features are represented by the high-level features of the VGG network, which are used to retain the spatial structure information of the content images.Figure 2
Model structure.


### 3.2. Image Semantic Content Representation

The network is generated based on a VGG network, which is used for image object recognition and localization. The structure of the feature extraction model is shown in Figure3. A feature space extracted from a normalized 19-layer VGG network (consisting of 16 convolutional layers and 5 pooling layers) is used. The network is normalized by weight scaling so that the average number of activations per convolutional filter over images and locations is equal to l. This move allows rescaling the VGG network without changing its output. This is because VGG contains only rectified linear activation functions without normalizing or merging the feature mappings. Let and x be the original image and the generated image, respectively, and let Pijl and Fijl represent the features in layer l. Then, the squared error loss between the two feature representations is defined as follows:(1)Lcontentp⟶,x⟶,l=12∑i,jFijl−Pijl2.Figure 3
Feature extraction model structure.With respect to the activation function in layerl, the derivative of the loss function is given as(2)dLconeentdFijl=Fl−Plij,ifFijl>0,0,ifFijl<0.The gradient with respect to the imagex can then be computed using standard error backpropagation. Thus, the initial random image x can be changed until it generates the same response in a particular layer of the convolutional neural network as the original image p. When convolutional neural networks are trained for object recognition, they form image representations so that the object information becomes increasingly clear along the processing hierarchy, referring to the feature responses in the deeper network as content representations.


### 3.3. Artistic Style Representation

To obtain a stylized representation of the input image, a feature space is used to capture the texture information of the image. This feature space can be built on top of the filter responses in any layer and consists of correlations between the different filter responses. These feature correlations are represented by the Gram matrixGijl', where Gijl is the inner product between the vectorized feature mappings i and j in the lth layer:(3)Gijl=∑kFiklFjkl.A stable, multiscale representation of the input image is obtained by considering multiple layers of feature correlations. The representation captures the texture information of the image, but not the global arrangement. The information captured by these stylized feature spaces constructed on different layers of the network can then be visualized by constructing images that match the stylized representation of a given input image. This is achieved by using gradient descent from a white noise image to minimize the mean square distance between the Gram matrix terms of the original image and the Gram matrix of the image to be generated. Leta and x be the original image and the generated image, while A and G denote the styles of the original and generated image layers l, respectively. Then, the loss of layer l can be expressed as(4)El=14Nl2Ml2∑i,jGijl−Aijl2.The total loss function has the following form:(5)Lstylea⟶,x⟶=∑lwlEl,where wl is the weighting factor. With respect to the activation function in layer l, the derivative of El can be expressed as(6)∂El∂Fijl=1Nl2Ml2FlTGl−AljiifFijl>0,0ifFijl<0.The gradient ofEl with respect to the pixel value x can be easily calculated using standard error back propagation.


### 3.4. Style Recognition

To transfer the style of the artwork to the photograph, a new image is synthesized that matches both the content representation and the style representation ofa. Therefore, the distance between the feature representation of the white noise image and the content representation of the photo in one layer and the distance between the style representation of the artwork defined on multiple layers of the convolutional neural network is jointly minimized. The minimized loss function is(7)Ltotalp⟶,a⟶,x⟶=αLcontentp⟶,x⟶+βLstylea⟶,x⟶,where α and β are the weighting factors for content and style reconstruction, respectively. The gradient about the pixel values can be used as input to the numerical optimization strategy, which is employed as L-BFGS. To extract image information in comparable proportions, the style image is always resized to the same size as the content image before computing the style features.


### 3.5. Mental State Recognition

Through the interaction layer,k-dimensional vectors are initialized n and they are used as auxiliary vectors for the input layer x. In the embedding layer, the vectors are multiplied with their corresponding features to obtain vi and then the n sets of vi are input to the interaction layer, and I is obtained by the interaction mode calculation, and w is the number of neurons in the interaction layer, which is determined by the following four interaction calculation modes. The number of neurons in the interaction layer is determined by the following 4 interaction calculation methods, and the output is as follows:(8)IDA=∑i=1n−1∑j=i+1nvi,vjxixj.The number of interactions isIDC, and the output as(9)IDC=v1,v2x1x2,…,vn−1,vnxn−1xnT.The interaction layer multiplies the corresponding positions ofvik and adds the corresponding products and the output as(10)IMA=∑i=1n−1∑j=i+1nvi1vj1xixj,…,∑i=1n−1∑j=i+1nvikvjkxixjT.The model studied in this article takes as input data the features selected by the image style transformation, that is,x=x1,x2,…,xi,…,xn. Then, the features are concatenated into the hidden layer together with the result after the FM module features are combined. The hidden layer has 5 layers with 128 neurons per layer, and the activation function ReLU; fully connected layers are used between the layers. Since the use of more fully connected layers and many parameters between layers makes the model complex, dropout is used in training the model. Dropout is randomly turning off some neurons so that some features are not overlearned by a certain neuron in the model, thus improving the robustness of the model. The percentage of FM-DNN using dropout to turn off neurons is 0.5. For the 4-classification task in this study, the number of neurons in the output layer is set to 4, and the output y is obtained, and the mental state recognition result y is output using the softmax layer. FM-DNN uses cross-entropy as(11)L=−∑i=14y^ilnyi,where y^i is the unique thermal coding label of the sample and yi is the probability of category i in the predicted value of the network. Stochastic gradient descent is used to train the parameters in the network, setting the batch size to 128, the maximum number of training rounds to 10, and the learning rate to 0.001. To prevent overfitting, an early stop method is used; that is, the training will end early when the accuracy of the validation set no longer rises for two rounds.


## 3.1. Model Architecture

The region-difference stylization model in this article is shown in Figure2. After the content image passes through the DeepLabV3 semantic segmentation network, a segmentation map with n semantic regions is generated, and different styles are adopted for stylization in each of these n regions. Based on the neural style conversion algorithm, a pretrained VGG-16 neural network is used to calculate the style loss and content loss to ensure the stylization effect while reducing the computational burden. In the loss function part, the content features are represented by the high-level features of the VGG network, which are used to retain the spatial structure information of the content images.Figure 2
Model structure.


## 3.2. Image Semantic Content Representation

The network is generated based on a VGG network, which is used for image object recognition and localization. The structure of the feature extraction model is shown in Figure3. A feature space extracted from a normalized 19-layer VGG network (consisting of 16 convolutional layers and 5 pooling layers) is used. The network is normalized by weight scaling so that the average number of activations per convolutional filter over images and locations is equal to l. This move allows rescaling the VGG network without changing its output. This is because VGG contains only rectified linear activation functions without normalizing or merging the feature mappings. Let and x be the original image and the generated image, respectively, and let Pijl and Fijl represent the features in layer l. Then, the squared error loss between the two feature representations is defined as follows:(1)Lcontentp⟶,x⟶,l=12∑i,jFijl−Pijl2.Figure 3
Feature extraction model structure.With respect to the activation function in layerl, the derivative of the loss function is given as(2)dLconeentdFijl=Fl−Plij,ifFijl>0,0,ifFijl<0.The gradient with respect to the imagex can then be computed using standard error backpropagation. Thus, the initial random image x can be changed until it generates the same response in a particular layer of the convolutional neural network as the original image p. When convolutional neural networks are trained for object recognition, they form image representations so that the object information becomes increasingly clear along the processing hierarchy, referring to the feature responses in the deeper network as content representations.


## 3.3. Artistic Style Representation

To obtain a stylized representation of the input image, a feature space is used to capture the texture information of the image. This feature space can be built on top of the filter responses in any layer and consists of correlations between the different filter responses. These feature correlations are represented by the Gram matrixGijl', where Gijl is the inner product between the vectorized feature mappings i and j in the lth layer:(3)Gijl=∑kFiklFjkl.A stable, multiscale representation of the input image is obtained by considering multiple layers of feature correlations. The representation captures the texture information of the image, but not the global arrangement. The information captured by these stylized feature spaces constructed on different layers of the network can then be visualized by constructing images that match the stylized representation of a given input image. This is achieved by using gradient descent from a white noise image to minimize the mean square distance between the Gram matrix terms of the original image and the Gram matrix of the image to be generated. Leta and x be the original image and the generated image, while A and G denote the styles of the original and generated image layers l, respectively. Then, the loss of layer l can be expressed as(4)El=14Nl2Ml2∑i,jGijl−Aijl2.The total loss function has the following form:(5)Lstylea⟶,x⟶=∑lwlEl,where wl is the weighting factor. With respect to the activation function in layer l, the derivative of El can be expressed as(6)∂El∂Fijl=1Nl2Ml2FlTGl−AljiifFijl>0,0ifFijl<0.The gradient ofEl with respect to the pixel value x can be easily calculated using standard error back propagation.


## 3.4. Style Recognition

To transfer the style of the artwork to the photograph, a new image is synthesized that matches both the content representation and the style representation ofa. Therefore, the distance between the feature representation of the white noise image and the content representation of the photo in one layer and the distance between the style representation of the artwork defined on multiple layers of the convolutional neural network is jointly minimized. The minimized loss function is(7)Ltotalp⟶,a⟶,x⟶=αLcontentp⟶,x⟶+βLstylea⟶,x⟶,where α and β are the weighting factors for content and style reconstruction, respectively. The gradient about the pixel values can be used as input to the numerical optimization strategy, which is employed as L-BFGS. To extract image information in comparable proportions, the style image is always resized to the same size as the content image before computing the style features.


## 3.5. Mental State Recognition

Through the interaction layer,k-dimensional vectors are initialized n and they are used as auxiliary vectors for the input layer x. In the embedding layer, the vectors are multiplied with their corresponding features to obtain vi and then the n sets of vi are input to the interaction layer, and I is obtained by the interaction mode calculation, and w is the number of neurons in the interaction layer, which is determined by the following four interaction calculation modes. The number of neurons in the interaction layer is determined by the following 4 interaction calculation methods, and the output is as follows:(8)IDA=∑i=1n−1∑j=i+1nvi,vjxixj.The number of interactions isIDC, and the output as(9)IDC=v1,v2x1x2,…,vn−1,vnxn−1xnT.The interaction layer multiplies the corresponding positions ofvik and adds the corresponding products and the output as(10)IMA=∑i=1n−1∑j=i+1nvi1vj1xixj,…,∑i=1n−1∑j=i+1nvikvjkxixjT.The model studied in this article takes as input data the features selected by the image style transformation, that is,x=x1,x2,…,xi,…,xn. Then, the features are concatenated into the hidden layer together with the result after the FM module features are combined. The hidden layer has 5 layers with 128 neurons per layer, and the activation function ReLU; fully connected layers are used between the layers. Since the use of more fully connected layers and many parameters between layers makes the model complex, dropout is used in training the model. Dropout is randomly turning off some neurons so that some features are not overlearned by a certain neuron in the model, thus improving the robustness of the model. The percentage of FM-DNN using dropout to turn off neurons is 0.5. For the 4-classification task in this study, the number of neurons in the output layer is set to 4, and the output y is obtained, and the mental state recognition result y is output using the softmax layer. FM-DNN uses cross-entropy as(11)L=−∑i=14y^ilnyi,where y^i is the unique thermal coding label of the sample and yi is the probability of category i in the predicted value of the network. Stochastic gradient descent is used to train the parameters in the network, setting the batch size to 128, the maximum number of training rounds to 10, and the learning rate to 0.001. To prevent overfitting, an early stop method is used; that is, the training will end early when the accuracy of the validation set no longer rises for two rounds.


## 4. Experiments and Results

### 4.1. Experiment Setup

The algorithms in this article were experimented on a nonpublic dataset from a domestic arts and crafts research institute in China. The proposed model is used as a model for the analysis of the role of arts and crafts in public mental health education, and experiments are conducted for a variety of styles of arts and crafts to identify the symbolic features in arts and crafts and to classify the images. The number of training sets was 4878, and the number of test sets was 2500. In order to expand the number of training sets and improve the model training effect, the training set is expanded by 4 times by rotating 90 degrees, 180 degrees, and 270 degrees for one of the arts and crafts in the training set. The Adam optimizer is used, the batch parameters are set to 16, the number of training rounds is 20, and the learning rate is 0.0001 in the first 10 training rounds and decays linearly from 0.0001 to 0 in the second 10 training rounds. The experimental environment is shown in Table1. Keras is a higher-order application programming interface built on top of a symbolic mathematical library that supports tensor computation. The supported underlying architectures including Keras can be used to rapidly build and train networks and can be used to train networks using either CPU or GPU, with GPU training requiring an NVIDIA graphics card and configuration to install the relevant environment, while the computational speed and efficiency are greatly improved. The training process performance enhancement and loss convergence are shown in Figure 4 and Figure 5.Table 1
Experimental environment.
NameVersionsPython3.7TensorFlow-gpu2.0.0-rc0CUDA10.0CUDNNV7.5.0Opencv-Python4.4.0.46Keras2.3.1Figure 4
Schematic diagram of training process performance improvement.Figure 5
The training process loss convergence schematic.


### 4.2. Experimental Results

The recognition rate of this network is 64.73%, and the total number of parameters is 58,423, corresponding to each emotion as shown in Table2.Table 2
Separable convolution recognition rate.
Types of emotionsRecognition rate (%)Anger58.23Disgust55.46Fear49.31Happy87.57Sadness53.83Surprised70.03Normal72.68After incorporating the residual blocks, the recognition rate of the method in this chapter can reach 70.89%, and the recognition rate and loss function are corresponding to each emotion as shown in Table3.Table 3
Experimental environment.
Types of emotionsRecognition rate (%)Anger61.54Disgust57.22Fear50.49Happy88.68Sadness55.35Surprised74.61Normal77.76We introduce spatial residual connectivity, referred to as RN, to the separable convolutional network and keep other conditions unchanged. From Tables2 and 3, we find that the separable convolutional network with RN shows significant improvement compared to the method without the inclusion of residuals. This is because the residual structure selects the appropriate neighbor range for each node and avoids the lack of differentiation of the nodes when the network stacks multiple layers. By introducing cross-domain spatial residual convolution, the spatial-temporal information can be enhanced, and the residuals also solve the problem of superposition of two convolutions. Next, we also we first implemented the simpler model structure of LeNet-5, and the accuracy of the model on the Fer2013 dataset was 58.3% with 1.168 million participants. Then, we performed classification experiments using VGG-16, with an accuracy of 68.81% on the Fer2013 dataset and a number of 14,754,000 parameters. While we used separable convolutional counts of only 58,000, the accuracy reached 64.17% without adding the residual network, and after adding the residual network, it brought the average recognition rate to 70.89%, an improvement of 6.72%, and both methods reached an average accuracy of 65% ± 5% on this dataset using the manual case. Better results were achieved with a smaller number of parameters and training time than the other methods. Among the recognition results, the highest recognition rate can be achieved for the happy emotion, which can reach 88.68%. The second one is normal emotion, and the recognition rate can reach 77.76%; and the third one is surprise, and the recognition rate can reach 73.61%; while the emotions with relatively low recognition rate are disgust, fear, and sadness, the facial changes of happiness and surprise are more exaggerated and disgust and sadness have less facial changes; happiness and surprise have low similarity with other expressions, while disgust and anger are more similar, and both have frown and grin, which are easy to misjudge; the number of disgust expressions in the training set is very small, only 436, which is not enough to learn enough features, making the results less satisfactory, while the number of happy is very large, so there is a high accuracy rate. To verify the superiority of this network, we also did experiments in fer2013, and the comparison results are shown in Figure 6.Figure 6
Comparison of recognition rate of deep separable residual convolution.In this article, we not only compare the accuracy of Bi-LSTM and CNN models, but also train the classical RNN model and LSTM model to help analyze the advantages, disadvantages, and effectiveness of the models in terms of applications in this article. The experimental results are shown in Figure7.Figure 7
Performance comparison.The Bi-LSTM achieves quite good results compared to the LSTM, which can be analyzed in context, and also has 5.52% higher accuracy compared to the CNN model, which is predicted to be due to the fact that the CNN does not have high accuracy in analyzing long segments due to the varying sentence lengths in the dataset. The RNN is significantly less accurate than the other three due to gradient disappearance and gradient explosion, and the LSTM is better compared to the RNN, but still cannot do accurate prediction because it cannot be combined with the following content. The results of this article indirectly illustrate the conclusion that the daily textual sentiment expressions of the Chinese public are scattered in the segments, and there is a high possibility of oversight if manual analysis is performed. 95.55% accuracy of Bi-LSTM can better achieve the ability of batch processing text.TheF1-score of the four variants of FM-DNN and the control model for each mental health class classification are shown in Figures 8 and 9. As can be seen from Figures 8 and 9, the F1-score of the six methods in no mental health and moderate mental health did not differ much; in mild mental health, the F1-score of the FM model was much worse than the other models, while FM-DNN (IDA) showed a better classification; in severe mental health, the F1-score of FM and DNN was lower, indicating that using only FM or DNN trained PCk-means selected 22 features; that is, data containing 13 important dimensions such as interpersonal stress, academic stress, and family education are not good for mental health identification. This may be since FM cannot effectively learn the complex nonlinear relationship between mental health and its related features, or the DNN model classifies a feature independently of other features to identify mental health and lacks the consideration of feature combination in mental health identification. In contrast, the classification performance of F-DNN in this article has a significant advantage over the control group, which not only shows that the diversity of dimensions has an important contribution to mental health recognition, but also shows that F-DNNN improves the diversity and comprehensiveness of prediction dimensions by using FM to effectively combine mental health features, thus enhancing the classification effect of the model. Compared with other models, F-DNN, especially IMA, can better identify severe mental health testers more accurately, which is valuable in the screening of mental health disorders. Therefore, the comparison with the control model shows that the DNN model designed in this study exhibits good classification ability, and the introduction of FM also has a significant effect on the optimization of the network structure.Figure 8
Comparison ofF1-score of severe mental health grade classification.Figure 9
Comparison ofF1-scores of mild mental health rating categories.


## 4.1. Experiment Setup

The algorithms in this article were experimented on a nonpublic dataset from a domestic arts and crafts research institute in China. The proposed model is used as a model for the analysis of the role of arts and crafts in public mental health education, and experiments are conducted for a variety of styles of arts and crafts to identify the symbolic features in arts and crafts and to classify the images. The number of training sets was 4878, and the number of test sets was 2500. In order to expand the number of training sets and improve the model training effect, the training set is expanded by 4 times by rotating 90 degrees, 180 degrees, and 270 degrees for one of the arts and crafts in the training set. The Adam optimizer is used, the batch parameters are set to 16, the number of training rounds is 20, and the learning rate is 0.0001 in the first 10 training rounds and decays linearly from 0.0001 to 0 in the second 10 training rounds. The experimental environment is shown in Table1. Keras is a higher-order application programming interface built on top of a symbolic mathematical library that supports tensor computation. The supported underlying architectures including Keras can be used to rapidly build and train networks and can be used to train networks using either CPU or GPU, with GPU training requiring an NVIDIA graphics card and configuration to install the relevant environment, while the computational speed and efficiency are greatly improved. The training process performance enhancement and loss convergence are shown in Figure 4 and Figure 5.Table 1
Experimental environment.
NameVersionsPython3.7TensorFlow-gpu2.0.0-rc0CUDA10.0CUDNNV7.5.0Opencv-Python4.4.0.46Keras2.3.1Figure 4
Schematic diagram of training process performance improvement.Figure 5
The training process loss convergence schematic.


## 4.2. Experimental Results

The recognition rate of this network is 64.73%, and the total number of parameters is 58,423, corresponding to each emotion as shown in Table2.Table 2
Separable convolution recognition rate.
Types of emotionsRecognition rate (%)Anger58.23Disgust55.46Fear49.31Happy87.57Sadness53.83Surprised70.03Normal72.68After incorporating the residual blocks, the recognition rate of the method in this chapter can reach 70.89%, and the recognition rate and loss function are corresponding to each emotion as shown in Table3.Table 3
Experimental environment.
Types of emotionsRecognition rate (%)Anger61.54Disgust57.22Fear50.49Happy88.68Sadness55.35Surprised74.61Normal77.76We introduce spatial residual connectivity, referred to as RN, to the separable convolutional network and keep other conditions unchanged. From Tables2 and 3, we find that the separable convolutional network with RN shows significant improvement compared to the method without the inclusion of residuals. This is because the residual structure selects the appropriate neighbor range for each node and avoids the lack of differentiation of the nodes when the network stacks multiple layers. By introducing cross-domain spatial residual convolution, the spatial-temporal information can be enhanced, and the residuals also solve the problem of superposition of two convolutions. Next, we also we first implemented the simpler model structure of LeNet-5, and the accuracy of the model on the Fer2013 dataset was 58.3% with 1.168 million participants. Then, we performed classification experiments using VGG-16, with an accuracy of 68.81% on the Fer2013 dataset and a number of 14,754,000 parameters. While we used separable convolutional counts of only 58,000, the accuracy reached 64.17% without adding the residual network, and after adding the residual network, it brought the average recognition rate to 70.89%, an improvement of 6.72%, and both methods reached an average accuracy of 65% ± 5% on this dataset using the manual case. Better results were achieved with a smaller number of parameters and training time than the other methods. Among the recognition results, the highest recognition rate can be achieved for the happy emotion, which can reach 88.68%. The second one is normal emotion, and the recognition rate can reach 77.76%; and the third one is surprise, and the recognition rate can reach 73.61%; while the emotions with relatively low recognition rate are disgust, fear, and sadness, the facial changes of happiness and surprise are more exaggerated and disgust and sadness have less facial changes; happiness and surprise have low similarity with other expressions, while disgust and anger are more similar, and both have frown and grin, which are easy to misjudge; the number of disgust expressions in the training set is very small, only 436, which is not enough to learn enough features, making the results less satisfactory, while the number of happy is very large, so there is a high accuracy rate. To verify the superiority of this network, we also did experiments in fer2013, and the comparison results are shown in Figure 6.Figure 6
Comparison of recognition rate of deep separable residual convolution.In this article, we not only compare the accuracy of Bi-LSTM and CNN models, but also train the classical RNN model and LSTM model to help analyze the advantages, disadvantages, and effectiveness of the models in terms of applications in this article. The experimental results are shown in Figure7.Figure 7
Performance comparison.The Bi-LSTM achieves quite good results compared to the LSTM, which can be analyzed in context, and also has 5.52% higher accuracy compared to the CNN model, which is predicted to be due to the fact that the CNN does not have high accuracy in analyzing long segments due to the varying sentence lengths in the dataset. The RNN is significantly less accurate than the other three due to gradient disappearance and gradient explosion, and the LSTM is better compared to the RNN, but still cannot do accurate prediction because it cannot be combined with the following content. The results of this article indirectly illustrate the conclusion that the daily textual sentiment expressions of the Chinese public are scattered in the segments, and there is a high possibility of oversight if manual analysis is performed. 95.55% accuracy of Bi-LSTM can better achieve the ability of batch processing text.TheF1-score of the four variants of FM-DNN and the control model for each mental health class classification are shown in Figures 8 and 9. As can be seen from Figures 8 and 9, the F1-score of the six methods in no mental health and moderate mental health did not differ much; in mild mental health, the F1-score of the FM model was much worse than the other models, while FM-DNN (IDA) showed a better classification; in severe mental health, the F1-score of FM and DNN was lower, indicating that using only FM or DNN trained PCk-means selected 22 features; that is, data containing 13 important dimensions such as interpersonal stress, academic stress, and family education are not good for mental health identification. This may be since FM cannot effectively learn the complex nonlinear relationship between mental health and its related features, or the DNN model classifies a feature independently of other features to identify mental health and lacks the consideration of feature combination in mental health identification. In contrast, the classification performance of F-DNN in this article has a significant advantage over the control group, which not only shows that the diversity of dimensions has an important contribution to mental health recognition, but also shows that F-DNNN improves the diversity and comprehensiveness of prediction dimensions by using FM to effectively combine mental health features, thus enhancing the classification effect of the model. Compared with other models, F-DNN, especially IMA, can better identify severe mental health testers more accurately, which is valuable in the screening of mental health disorders. Therefore, the comparison with the control model shows that the DNN model designed in this study exhibits good classification ability, and the introduction of FM also has a significant effect on the optimization of the network structure.Figure 8
Comparison ofF1-score of severe mental health grade classification.Figure 9
Comparison ofF1-scores of mild mental health rating categories.


## 5. Conclusion

Arts and crafts both reflect and exercise various mental abilities such as attention, observation, imagination, memory, and thinking, and also implicate various mental qualities such as interest, emotion, will, and character, as well as activate the right brain. By exploring the potential of arts and crafts education to develop brain potential and promote mental health, we can improve the public’s sensibility through arts and crafts activities, prompt them to have a deeper understanding of classic works and themselves, become more sensitive to their own minds, and make their mental feelings more active and positive, so that brain potential, especially right brain potential, can be further developed, thus enriching the form of mental health education. Therefore, seeking the organic combination of arts and crafts education and mental health education should become the direction of joint efforts between arts and crafts education and mental health education in the future. The analysis of the role of arts and crafts in public mental health education based on artificial intelligence technology proposed in this article proves that arts and crafts analysis, as an effective white ego analysis technique, is an effective psychological test and psychotherapy method, and its application to mental health education courses can guide the public to think deeply about self-awareness, improve the level of self-awareness, and promote the healthy development of the personality of the general public in order to achieve the purpose of mental health education. In the future, we plan to carry out an analysis study of the role of arts and crafts in public mental health education using recurrent neural networks and knowledge mapping.



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*Source: 9201892-2022-08-31.xml*