# Evaluation Model of Music Therapy’s Auxiliary Effect on Mental Health Based on Artificial Intelligence Technology

Authors: Dan Lu
Journal: Journal of Environmental and Public Health
 (2022)

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## Abstract

The reason why music can affect people’s emotional experience is that the stimulation can be transmitted to the brain through hearing, such as the thalamus and lenticular nucleus. Music therapy has a positive auxiliary treatment effect on mental health. Therefore, an evaluation model of the auxiliary effect of music therapy on mental health based on artificial intelligence technology is proposed. We construct the constraint index parameters for the evaluation of music therapy’s auxiliary effect on mental health, take parent pressure, self-pressure, teacher pressure, and social pressure as the questionnaire object parameters, take class type as the independent variable, carry out an independent samplet-test, and construct an adaptive information extraction model for the evaluation of music therapy’s auxiliary effect on mental health. Paired sample t-test is used to analyze whether there is a difference between the experimental group and the control group on the learning stress scale. According to the analysis of the difference between the experimental group and the control group, combined with the difference test analysis of the data of the stress release of music therapy on mental health, the quantitative evaluation of the auxiliary effect of music therapy on mental health is realized through artificial intelligence optimization control. The experimental results show that the accuracy and reliability of this method to analyze the auxiliary effect of music therapy on mental health are high. There are obvious changes in the data of students’ self-pressure, and the difference between the average value and standard deviation of the data before and after the course is obvious. From the perspective of the effectiveness of the course, the students in the class who implement the four relaxation experience courses in the course are under the pressure of parents, self-pressure, teacher pressure, and social pressure. There are obvious changes in the five aspects of learning pressure compared with that before the implementation of the course. After the course experience, the pressure value of most students decreases, and the course intervention effect is obvious.


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## Body

## 1. Introduction

Music therapy integrates music, medicine, and psychology and is an interdisciplinary subject. From the perspective of music therapy, the sensory stimulation brought by music can trigger multiple sensory experiences. Different music can make people produce corresponding physiological reactions and cause corresponding emotional experiences. Studies have found that the reason why music can affect people’s emotional experience is that music can transmit stimuli to the brain through hearing, such as the thalamus and lenticular nucleus [1, 2]. These brain regions contain dopaminergic neurons, and the dopamine secreted by them is an important neurotransmitter in the brain. Music stimulates the secretion of dopamine and participates in many physiological activities such as people’s movement, cognition, emotion, positive reinforcement, and endocrine regulation.At present, the application of music therapy in schools is gradually increasing. Researchers are aware of the great influence of music on people’s psychology and the link between music education and mental health. Some researchers have adopted music therapy to solve the psychological problems of students’ test anxiety and achieved certain results [3, 4] and applied a certain method of music therapy to the music class and integrated it with the teaching content. However, in these studies, researchers have applied a variety of methods at the same time, and it is difficult to determine the main methods to achieve practical results in the study. On the basis of previous studies, the author will use the methods of receptive music therapy to explore the intervention of middle school students’ multiple psychological stress experiences [5, 6]. Nowadays, the social environment is complex, the pace of life is accelerating, the learning competition is fierce, and the media communication channels are increasing. The growth problems and puzzles faced by middle school students in such a social environment are also increasing. At present, China is promoting the reform of quality education in an all-round way [7]. However, due to the time and cycle of the reform, it is impossible for primary and secondary education to get rid of the shackles of examination-oriented education. Society, schools, parents, and teachers have high expectations for students’ academic achievements and attach importance to the transmission of knowledge and the cultivation of intellectual factors. However, in fact, physical and mental health, especially mental health and sufficient pressure resistance, are the important foundation for students to become adults and talents. The middle school stage is a critical period that determines a person’s future development. Middle school students are faced with pressure from all aspects. They not only face the intense competitive pressure of the entrance examination but also deal with interpersonal relationships with teachers, classmates, and parents [8]. In the face of this incident, students are under a lot of psychological pressure, and the pressure can not be relieved. Finally, psychological problems such as truancy, weariness, anxiety, irritability, and depression will occur. At present, it is still an important feature of education in this era to define middle school students by academic achievement as the evaluation standard. Therefore, most of the psychological problems produced by students spread out indefinitely, becoming one of the most prominent psychological problems of middle school students, and also the key and difficult problems to be solved in the school mental health education and work.In view of this, this paper proposes an artificial intelligence technology-based evaluation model of the auxiliary effect of music therapy on mental health. We construct the constraint index parameters for the evaluation of music therapy’s auxiliary effect on mental health, conduct an independent samplet-test, use the big data mining method to analyze the artificial intelligence control of music therapy’s auxiliary effect on mental health, combine the difference test and analysis of the data of music therapy’s pressure release on mental health, and realize the quantitative evaluation of music therapy’s auxiliary effect on mental health through artificial intelligence optimization control. Finally, the empirical analysis is carried out and the conclusion of effectiveness is drawn.


## 2. Big Data Mining Theory

In the era of big data, data mining is the most critical work. Big data mining is a process of discovering valuable and potentially useful information and knowledge from massive, noisy, fuzzy, and random large databases, and it is also a decision support process. It is mainly based on artificial intelligence, machine learning, pattern learning, statistics, and so on. Through highly automated analysis of big data, inductive reasoning is made, and potential patterns are mined. Big data mining needs to choose the corresponding solution according to the actual demand and provide stable support for the subsequent data processing. Big data mining can be handled in different ways according to different data types. The most common data type in big data mining applications is called structured data, which is defined as the data stored in the database and can be logically expressed by a two-dimensional table structure. Structured data is processed very quickly because of its regular format. Text is the largest recorded data form at present, such as text content in web pages, chat records, e-mails, and various documents of enterprises. They contain a lot of valuable information, and their analysis and processing give birth to natural language processing. The most difficult thing in big data processing is multimedia unstructured data, including images, voices, and videos. Deep mining and understanding of these data can produce many novel and practical functions, such as automatic monitoring, face recognition, and automatic driving. These fields are also potential fields for big data mining applications in the future. According to the analysis of the attributes and characteristics of objects, different classes are established to describe things, the internal rules are identified and analyzed, and the objects are divided into several classes according to these rules. Association is a kind of connection that something happens when other things happen. We grasp the law of the development of the analysis object and predict the future trend. Data mining methods can be divided into statistical methods, machine learning methods, neural network methods, and database methods. Statistics can be subdivided into regression analysis, discriminant analysis, cluster analysis, exploratory analysis, fuzzy sets, rough sets, support vector machines, and so on.The basic principle of artificial intelligence technology is that artificial intelligence is equal to mathematical calculation. Artificial intelligence (AI) is a new technical science that studies and develops theoretical methods for simulating, extending, and expanding human intelligence. Artificial intelligence is a branch of computer science. It attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence. The research in this field includes robots, language recognition, image recognition, natural language processing, and expert systems. Since the birth of artificial intelligence, theory and technology have become more and more mature, and the application field has also expanded. Artificial intelligence can simulate the information process of human consciousness and thinking. Artificial intelligence is not human intelligence, but it can think like humans and may exceed human intelligence.


## 3. Statistical Data and Characteristic Analysis of the Evaluation Model of Music’s Auxiliary Effect on Mental Health

### 3.1. Research Methods

Under the guidance of the new music curriculum goal, the music curriculum should be a powerful method to regulate students’ mental health, besides the traditional aesthetic significance. Music is used to relieve middle school students’ exam anxiety, and in order to make music play its established effect, the researchers used the related methods of receptive music therapy, which went through a series of course intervention processes such as making plans, pretesting, pretesting, and posttesting [9]. In the classroom, they used receptive music therapy methods such as the “relaxation training method,” “music imagination method,” and “music emotion resonance method” to guide students’ relaxation, emotional guidance, and imagination guidance. The feasibility of music therapy in music class is clarified. Luo Tong 6 also introduced the method of receptive music therapy in middle school music class. She realized that modern middle school students’ psychological problems were getting worse, but they did not have the corresponding self-guidance ability:(1)
Interview method: through targeted visits to teachers, students, and parents of students, we can learn about the learning and living conditions of middle school students, find and record the problems and difficulties that students encounter in their learning and living, and collect more comprehensive information for designing the course teaching in the thesis [10].(2)
Investigation method: through the relevant information, targeted questionnaires are selected to investigate the current situation of students’ learning pressure in Zibo Middle School, so as to have a complete understanding of students’ psychological status. Through the statistics, classification, induction, and analysis of the survey data, we can find out the problems, provide effective data support for the paper writing, and provide a factual basis for the countermeasures to solve the problems [11].(3)
Observation method: the observation method refers to a research method of observing, recording, and analyzing the natural behaviors and phenomena of the observed objects in a purposeful and planned way under natural conditions. As the course implementer in the experimental process, the author participated in the course all the time. The observation method can be used to analyze the performance of the participants in the course, so as to provide feedback for further research program implementation [12].


### 3.2. Statistical Data Modeling of the Evaluation Model of Music’s Auxiliary Effect on Mental Health

In an early human society with limited aesthetic significance, music has already reached an indispensable position because human beings believe that music has the function of dispelling diseases and strengthening the body, and it is of great significance to human survival [13]. The music itself contains multiple stimulating experiences that human beings can feel, including auditory stimulation, visual stimulation, and tactile stimulation. These stimulating experiences make the human body produce different physiological and psychological reactions, such as heart rate and blood pressure, increase or decrease of endocrine substances, muscle reaction, respiratory rate, and brain waves. In view of the differences in life background, age, personality, preferences, and other factors, the same concert has different physiological and psychological effects on different people. Therefore, music therapists often use different music to intervene and treat visitors from different degrees and angles. In order to realize the evaluation of music therapy’s auxiliary effect on mental health based on data sharing, the optimal selection model of music therapy’s auxiliary effect on mental health is constructed, and the big data fusion analysis method is used to evaluate music therapy’s auxiliary effect on mental health. Combined with the fusion cluster analysis method, the big data sampling model of music therapy’s auxiliary effect on mental health is established, and the evaluation model of music therapy’s auxiliary effect on mental health is statistically analyzed [14]. The optimal fusion characteristic parameter analysis model for the evaluation of music therapy’s auxiliary effect on mental health is constructed. It is assumed that the phase space distribution W of the big data of music therapy’s auxiliary effect on mental health is an n × m fuzzy control matrix. Under the optimization decision of music therapy’s auxiliary effect on mental health, the parameter set of music therapy’s auxiliary effect on mental health is described as Pni=pk|prkj=1,k=1,2,…,m by fuzzy statistical analysis method, and the constraint index parameter set of music therapy’s auxiliary effect on mental health is constructed as vi. The sample set is as follows:(1)Wi¯=1m∑q=1mWvi,pq.In formula (1), m is the heart rate and blood pressure, vi is the auditory stimulation guiding parameter, pq is the visual stimulation guiding parameter, S is the tactile stimulation process parameter, Wvi,pq represents the task set of big data scheduling for the evaluation of music therapy’s auxiliary effect on mental health, and Cvi,vj represents the characteristic distribution set of music therapy’s auxiliary effect on mental health. Under the fusion clustering mode, the constraint index parameters of music therapy’s auxiliary effect on mental health are as follows [15–17]:(2)xt=∑i=0paθisit+nt.In formula (2), p is the conditional probability distribution number of the evaluation mode of music therapy’s auxiliary effect on mental health, nt is the characteristic distribution set of music therapy’s auxiliary effect on mental health, and sit is the big data statistical characteristic quantity of music therapy’s auxiliary effect on mental health. K-means clustering is used to cluster music therapy mental health data, randomly set the center points of K categories, classify each data point to the nearest center point, recalculate the center points of each category according to the clustering results, and then construct new K categories according to the new center points. After many iterations, the center point will eventually converge and stop moving, thus completing data clustering. The clustering process is shown in Figure 1.Figure 1
Music therapy mental health data clustering process.According to Figure1, the fuzzy spatial structure reorganization method is adopted to construct the characteristic distribution set of music therapy’s auxiliary effect on mental health, and the fuzzy membership function is obtained:(3)Rs0=∑n=0kRsn,dγndγn+Rsk+1.In formula (3), Rsn represents the scale information amount of music therapy’s evaluation of mental health assistance, dγn is the dimension of mental health assistance data reconstruction, and Rsk+1 is the regression coefficient of music therapy’s evaluation of mental health assistance. Different resonance sound waves of music will cause different physiological responses in the human body, for example, the increase or decrease of pulse blood pressure, the increase or decrease of breathing rate, the increase or decrease of hormone secretion, and the contraction or relaxation of muscles. In the past, these reactions will have a lasting impact on the human body and then play a therapeutic role. By combining the multiscale feature decomposition method, the optimal parameter distribution set for evaluating the auxiliary effect of music therapy on mental health is obtained as follows:(4)Gt=minG1t+G2t=min−∫Fμt×signkμt+w∫ΔTmtKμ∈Θ.In formula (4), kμt represents the sampled pain nerve parameters of statistical big data in the evaluation mode of the auxiliary effect of music therapy on mental health at T time; ΔTmt is the distribution characteristic set of evaluation indexes representing the auxiliary effect of music therapy on mental health at time T; w is the relative weight of reliable distribution of monitoring treatment after anesthesia; kμt is the probability condition of. By introducing the observation sets userInput, sanitizex, and flipCoin of music therapy’s auxiliary effect on mental health, a big data information fusion model is obtained. According to the results of big data fusion and feature sampling, the statistical data of music therapy’s auxiliary effect on the mental health evaluation model is modeled [18, 19].


### 3.3. Data Feature Extraction Based on Artificial Intelligence

It has a great influence on musicians’ emotions. Lively music makes people happy physically and mentally, sad and gloomy music can easily arouse people’s sad feelings, high music can stimulate people’s fighting spirit, and exhausted music can make people lose their vitality. The quality of a person’s mood affects a person’s way of thinking, the angle of seeing problems, and so on. Therefore, music therapists only need to make rational use of this point and use different music to influence people’s emotions. According to the mining results of music therapy’s evaluation information on mental health assistance, this paper reconstructs the association rules of frequent itemsets of music therapy’s evaluation data on mental health assistance, analyzes the distribution set of performance indicators of music therapy’s evaluation on mental health assistance, and obtains the updated rules of music therapy’s evaluation on mental health assistance by multiple regression analysis [20]:(5)pjt+1=a1pjt+a2pgta1+a2,(6)mbestt+1=1n∑j=1npjt,(7)Xjt+1=pjt+1±β×mbestt+1−Xjt×ln1ujt+1.In formulas (5) to (7), a1 and a2 are the auxiliary dynamic components of receptive music therapy, pjt is the application parameter of recreative music therapy, pgt is the music rhythm, β is the interaction parameter between people, uj is the guiding music imagination parameter, Xjt is the component of consciousness transformation state, and Xjt is the association rule set of the evaluation of the auxiliary effect of music therapy on mental health for the first time [21, 22]. After the evaluation of the auxiliary effect of music therapy on mental health, the adaptive adjustment model is as follows:(8)minF=R2+A∑iξisubject.to:ϕxi−o2≤R2+ξiandξi≥0,i=1,2,…,(9)max∑iαiKxi,xi−∑i∑jαiαjKxi,xjsubject.to:∑iαi=1and0≤αi≤A,i=1,2,….In formulas (8) and (9), R is the self-state adjustment factor, ϕxi is the influence factor of music to relieve people’s tension, A, ξi are language guiding components, and pjt+1 is the established content imagination experience parameter. In the optimal cluster center, using the statistical distribution set representing the t+1-th cluster center, the statistical characteristic quantity of music therapy’s auxiliary effect evaluation on mental health is pgt. In the M-dimensional random vector, the adaptive characteristic component of performance index evaluation of music therapy’s auxiliary effect on mental health is defined as(10)pgt=argminfpjt|j=1,2,⋯,n.In formula (10), fpjt is the statistical characteristic quantity of music therapy’s auxiliary effect on mental health evaluation, thus realizing the big data characteristic analysis of performance index evaluation. Musical imagination is one of the commonly used therapeutic methods in receptive music. Under the guidance of therapists, visitors imagine music, accompanied by corresponding situational music [23, 24]. All the imaginary content is guided and controlled by the therapist. After the visitor enters the state of consciousness transformation, the therapist guides the language with the music, and the visitor imagines the established content in the music. Imagination content is the most beautiful natural scenery and self-experience, mostly used for physical relaxation and psychological decompression.


## 3.1. Research Methods

Under the guidance of the new music curriculum goal, the music curriculum should be a powerful method to regulate students’ mental health, besides the traditional aesthetic significance. Music is used to relieve middle school students’ exam anxiety, and in order to make music play its established effect, the researchers used the related methods of receptive music therapy, which went through a series of course intervention processes such as making plans, pretesting, pretesting, and posttesting [9]. In the classroom, they used receptive music therapy methods such as the “relaxation training method,” “music imagination method,” and “music emotion resonance method” to guide students’ relaxation, emotional guidance, and imagination guidance. The feasibility of music therapy in music class is clarified. Luo Tong 6 also introduced the method of receptive music therapy in middle school music class. She realized that modern middle school students’ psychological problems were getting worse, but they did not have the corresponding self-guidance ability:(1)
Interview method: through targeted visits to teachers, students, and parents of students, we can learn about the learning and living conditions of middle school students, find and record the problems and difficulties that students encounter in their learning and living, and collect more comprehensive information for designing the course teaching in the thesis [10].(2)
Investigation method: through the relevant information, targeted questionnaires are selected to investigate the current situation of students’ learning pressure in Zibo Middle School, so as to have a complete understanding of students’ psychological status. Through the statistics, classification, induction, and analysis of the survey data, we can find out the problems, provide effective data support for the paper writing, and provide a factual basis for the countermeasures to solve the problems [11].(3)
Observation method: the observation method refers to a research method of observing, recording, and analyzing the natural behaviors and phenomena of the observed objects in a purposeful and planned way under natural conditions. As the course implementer in the experimental process, the author participated in the course all the time. The observation method can be used to analyze the performance of the participants in the course, so as to provide feedback for further research program implementation [12].


## 3.2. Statistical Data Modeling of the Evaluation Model of Music’s Auxiliary Effect on Mental Health

In an early human society with limited aesthetic significance, music has already reached an indispensable position because human beings believe that music has the function of dispelling diseases and strengthening the body, and it is of great significance to human survival [13]. The music itself contains multiple stimulating experiences that human beings can feel, including auditory stimulation, visual stimulation, and tactile stimulation. These stimulating experiences make the human body produce different physiological and psychological reactions, such as heart rate and blood pressure, increase or decrease of endocrine substances, muscle reaction, respiratory rate, and brain waves. In view of the differences in life background, age, personality, preferences, and other factors, the same concert has different physiological and psychological effects on different people. Therefore, music therapists often use different music to intervene and treat visitors from different degrees and angles. In order to realize the evaluation of music therapy’s auxiliary effect on mental health based on data sharing, the optimal selection model of music therapy’s auxiliary effect on mental health is constructed, and the big data fusion analysis method is used to evaluate music therapy’s auxiliary effect on mental health. Combined with the fusion cluster analysis method, the big data sampling model of music therapy’s auxiliary effect on mental health is established, and the evaluation model of music therapy’s auxiliary effect on mental health is statistically analyzed [14]. The optimal fusion characteristic parameter analysis model for the evaluation of music therapy’s auxiliary effect on mental health is constructed. It is assumed that the phase space distribution W of the big data of music therapy’s auxiliary effect on mental health is an n × m fuzzy control matrix. Under the optimization decision of music therapy’s auxiliary effect on mental health, the parameter set of music therapy’s auxiliary effect on mental health is described as Pni=pk|prkj=1,k=1,2,…,m by fuzzy statistical analysis method, and the constraint index parameter set of music therapy’s auxiliary effect on mental health is constructed as vi. The sample set is as follows:(1)Wi¯=1m∑q=1mWvi,pq.In formula (1), m is the heart rate and blood pressure, vi is the auditory stimulation guiding parameter, pq is the visual stimulation guiding parameter, S is the tactile stimulation process parameter, Wvi,pq represents the task set of big data scheduling for the evaluation of music therapy’s auxiliary effect on mental health, and Cvi,vj represents the characteristic distribution set of music therapy’s auxiliary effect on mental health. Under the fusion clustering mode, the constraint index parameters of music therapy’s auxiliary effect on mental health are as follows [15–17]:(2)xt=∑i=0paθisit+nt.In formula (2), p is the conditional probability distribution number of the evaluation mode of music therapy’s auxiliary effect on mental health, nt is the characteristic distribution set of music therapy’s auxiliary effect on mental health, and sit is the big data statistical characteristic quantity of music therapy’s auxiliary effect on mental health. K-means clustering is used to cluster music therapy mental health data, randomly set the center points of K categories, classify each data point to the nearest center point, recalculate the center points of each category according to the clustering results, and then construct new K categories according to the new center points. After many iterations, the center point will eventually converge and stop moving, thus completing data clustering. The clustering process is shown in Figure 1.Figure 1
Music therapy mental health data clustering process.According to Figure1, the fuzzy spatial structure reorganization method is adopted to construct the characteristic distribution set of music therapy’s auxiliary effect on mental health, and the fuzzy membership function is obtained:(3)Rs0=∑n=0kRsn,dγndγn+Rsk+1.In formula (3), Rsn represents the scale information amount of music therapy’s evaluation of mental health assistance, dγn is the dimension of mental health assistance data reconstruction, and Rsk+1 is the regression coefficient of music therapy’s evaluation of mental health assistance. Different resonance sound waves of music will cause different physiological responses in the human body, for example, the increase or decrease of pulse blood pressure, the increase or decrease of breathing rate, the increase or decrease of hormone secretion, and the contraction or relaxation of muscles. In the past, these reactions will have a lasting impact on the human body and then play a therapeutic role. By combining the multiscale feature decomposition method, the optimal parameter distribution set for evaluating the auxiliary effect of music therapy on mental health is obtained as follows:(4)Gt=minG1t+G2t=min−∫Fμt×signkμt+w∫ΔTmtKμ∈Θ.In formula (4), kμt represents the sampled pain nerve parameters of statistical big data in the evaluation mode of the auxiliary effect of music therapy on mental health at T time; ΔTmt is the distribution characteristic set of evaluation indexes representing the auxiliary effect of music therapy on mental health at time T; w is the relative weight of reliable distribution of monitoring treatment after anesthesia; kμt is the probability condition of. By introducing the observation sets userInput, sanitizex, and flipCoin of music therapy’s auxiliary effect on mental health, a big data information fusion model is obtained. According to the results of big data fusion and feature sampling, the statistical data of music therapy’s auxiliary effect on the mental health evaluation model is modeled [18, 19].


## 3.3. Data Feature Extraction Based on Artificial Intelligence

It has a great influence on musicians’ emotions. Lively music makes people happy physically and mentally, sad and gloomy music can easily arouse people’s sad feelings, high music can stimulate people’s fighting spirit, and exhausted music can make people lose their vitality. The quality of a person’s mood affects a person’s way of thinking, the angle of seeing problems, and so on. Therefore, music therapists only need to make rational use of this point and use different music to influence people’s emotions. According to the mining results of music therapy’s evaluation information on mental health assistance, this paper reconstructs the association rules of frequent itemsets of music therapy’s evaluation data on mental health assistance, analyzes the distribution set of performance indicators of music therapy’s evaluation on mental health assistance, and obtains the updated rules of music therapy’s evaluation on mental health assistance by multiple regression analysis [20]:(5)pjt+1=a1pjt+a2pgta1+a2,(6)mbestt+1=1n∑j=1npjt,(7)Xjt+1=pjt+1±β×mbestt+1−Xjt×ln1ujt+1.In formulas (5) to (7), a1 and a2 are the auxiliary dynamic components of receptive music therapy, pjt is the application parameter of recreative music therapy, pgt is the music rhythm, β is the interaction parameter between people, uj is the guiding music imagination parameter, Xjt is the component of consciousness transformation state, and Xjt is the association rule set of the evaluation of the auxiliary effect of music therapy on mental health for the first time [21, 22]. After the evaluation of the auxiliary effect of music therapy on mental health, the adaptive adjustment model is as follows:(8)minF=R2+A∑iξisubject.to:ϕxi−o2≤R2+ξiandξi≥0,i=1,2,…,(9)max∑iαiKxi,xi−∑i∑jαiαjKxi,xjsubject.to:∑iαi=1and0≤αi≤A,i=1,2,….In formulas (8) and (9), R is the self-state adjustment factor, ϕxi is the influence factor of music to relieve people’s tension, A, ξi are language guiding components, and pjt+1 is the established content imagination experience parameter. In the optimal cluster center, using the statistical distribution set representing the t+1-th cluster center, the statistical characteristic quantity of music therapy’s auxiliary effect evaluation on mental health is pgt. In the M-dimensional random vector, the adaptive characteristic component of performance index evaluation of music therapy’s auxiliary effect on mental health is defined as(10)pgt=argminfpjt|j=1,2,⋯,n.In formula (10), fpjt is the statistical characteristic quantity of music therapy’s auxiliary effect on mental health evaluation, thus realizing the big data characteristic analysis of performance index evaluation. Musical imagination is one of the commonly used therapeutic methods in receptive music. Under the guidance of therapists, visitors imagine music, accompanied by corresponding situational music [23, 24]. All the imaginary content is guided and controlled by the therapist. After the visitor enters the state of consciousness transformation, the therapist guides the language with the music, and the visitor imagines the established content in the music. Imagination content is the most beautiful natural scenery and self-experience, mostly used for physical relaxation and psychological decompression.


## 4. Optimization of the Evaluation Model of Music Therapy’s Auxiliary Effect on Mental Health

### 4.1. Artificial Intelligence Control of Music Therapy for the Evaluation of the Auxiliary Effect of Mental Health

According to the different characteristics of parents’ pressure, self-pressure, teachers’ pressure, social pressure, and study pressure between the experimental group and the control group, combined with the difference test and analysis of some data on music therapy’s pressure release on mental health, through artificial intelligence optimization control, the statistical information analysis model of music therapy’s auxiliary effect on mental health is constructed, and the evaluation model of music therapy’s auxiliary effect on mental health is optimized [25, 26]. This paper puts forward an evaluation model of music therapy’s auxiliary effect on mental health based on data sharing. This paper reconstructs the association rules of frequent itemsets for the evaluation data of music therapy’s auxiliary effect on mental health, extracts the association feature information of music therapy’s auxiliary effect on mental health, adaptively updates and optimizes the evaluation feature quantity of performance indicators of music therapy’s auxiliary effect on mental health, and filters the evaluation feature quantity of music therapy’s auxiliary effect on mental health considering the constraints of association rules. The information update rules of statistical data are as follows:(11)λ=11+α∂S/∂t2,k∧μt+1=k∧μt+Qt+1×∂F^μ/Mg∂t−∂S∂tk∧μt,wherein(12)Qt+1=Pt+1∂S∂t,Pt+1=1λPt−P2t∂S/∂t2λ+Pt∂S/∂t2,(13)∂S∂t=rvc∂ωw∂t.In formulas (12) and (13), λ is the constraint index parameter set representing the evaluation of music therapy’s auxiliary effect on mental health, F^μ is the fuzzy membership function of music therapy’s auxiliary effect on mental health, ωw is the adaptive weighting coefficient, k^μt represents the regression analysis value of music therapy’s auxiliary effect on mental health at time T, Pt is the correlation inverse matrix, and α is the residual characteristic quantity. The data link of the evaluation of music therapy’s auxiliary effect on mental health is generated, metadata is generated as S=1,2,…,N, and the process optimization scheduling of music therapy’s auxiliary effect on mental health is realized by using the association rule mining method and the optimal mode selection method.


### 4.2. Evaluation Output of Music Therapy’s Auxiliary Effect on Mental Health

Correlation characteristic information of music therapy’s auxiliary effect on mental health is extracted using the optimization control method to evaluate the auxiliary effect of music therapy on mental health. The fuzzy control function of music therapy’s auxiliary effect on mental health is as follows:(14)Mv=w1∑i=1m×nHi−Si+Mhw2∑i=1m×nSi−Vi+w3∑i=1m×nVi−Hi.In formula (14), the evaluation component of music therapy’s auxiliary effect on mental health is Mh. After a group of clustering attribute characteristics are generated, the emotion management method is adopted to realize the evaluation and regression analysis of music therapy’s auxiliary effect on mental health. The piecewise regression analysis model is adopted, and the performance evaluation model is expressed as follows:(15)xi=0,M−∑j=1n/2wj−∑j=⌊n/2⌋+1iwj−∑j=i+1kwj<0,i≤k,fiM,n,w,c,r=minfM,n,w,c,r1,M−∑j=1n/2wj−∑j=⌊n/2⌋+1iwj−∑j=i+1kwj>0.In formula (15), M is the constraint parameter of situational music, and n,w,c,r are negative, tired, rebellious, and tired of learning reaction parameters, and wj is a comprehensive and dispersive stressor and a social interpersonal stress parameter. Through piecewise regression analysis and statistical analysis, the evaluation and optimization of music therapy’s auxiliary effect on mental health can be realized. According to the differences between the experimental group and the control group in parents’ pressure, self-pressure, teachers’ pressure, social pressure, and study pressure, combined with the difference test and analysis of some data of stress release of music therapy on mental health, the quantitative evaluation of the auxiliary effect of music therapy on mental health was realized through artificial intelligence optimization control.


## 4.1. Artificial Intelligence Control of Music Therapy for the Evaluation of the Auxiliary Effect of Mental Health

According to the different characteristics of parents’ pressure, self-pressure, teachers’ pressure, social pressure, and study pressure between the experimental group and the control group, combined with the difference test and analysis of some data on music therapy’s pressure release on mental health, through artificial intelligence optimization control, the statistical information analysis model of music therapy’s auxiliary effect on mental health is constructed, and the evaluation model of music therapy’s auxiliary effect on mental health is optimized [25, 26]. This paper puts forward an evaluation model of music therapy’s auxiliary effect on mental health based on data sharing. This paper reconstructs the association rules of frequent itemsets for the evaluation data of music therapy’s auxiliary effect on mental health, extracts the association feature information of music therapy’s auxiliary effect on mental health, adaptively updates and optimizes the evaluation feature quantity of performance indicators of music therapy’s auxiliary effect on mental health, and filters the evaluation feature quantity of music therapy’s auxiliary effect on mental health considering the constraints of association rules. The information update rules of statistical data are as follows:(11)λ=11+α∂S/∂t2,k∧μt+1=k∧μt+Qt+1×∂F^μ/Mg∂t−∂S∂tk∧μt,wherein(12)Qt+1=Pt+1∂S∂t,Pt+1=1λPt−P2t∂S/∂t2λ+Pt∂S/∂t2,(13)∂S∂t=rvc∂ωw∂t.In formulas (12) and (13), λ is the constraint index parameter set representing the evaluation of music therapy’s auxiliary effect on mental health, F^μ is the fuzzy membership function of music therapy’s auxiliary effect on mental health, ωw is the adaptive weighting coefficient, k^μt represents the regression analysis value of music therapy’s auxiliary effect on mental health at time T, Pt is the correlation inverse matrix, and α is the residual characteristic quantity. The data link of the evaluation of music therapy’s auxiliary effect on mental health is generated, metadata is generated as S=1,2,…,N, and the process optimization scheduling of music therapy’s auxiliary effect on mental health is realized by using the association rule mining method and the optimal mode selection method.


## 4.2. Evaluation Output of Music Therapy’s Auxiliary Effect on Mental Health

Correlation characteristic information of music therapy’s auxiliary effect on mental health is extracted using the optimization control method to evaluate the auxiliary effect of music therapy on mental health. The fuzzy control function of music therapy’s auxiliary effect on mental health is as follows:(14)Mv=w1∑i=1m×nHi−Si+Mhw2∑i=1m×nSi−Vi+w3∑i=1m×nVi−Hi.In formula (14), the evaluation component of music therapy’s auxiliary effect on mental health is Mh. After a group of clustering attribute characteristics are generated, the emotion management method is adopted to realize the evaluation and regression analysis of music therapy’s auxiliary effect on mental health. The piecewise regression analysis model is adopted, and the performance evaluation model is expressed as follows:(15)xi=0,M−∑j=1n/2wj−∑j=⌊n/2⌋+1iwj−∑j=i+1kwj<0,i≤k,fiM,n,w,c,r=minfM,n,w,c,r1,M−∑j=1n/2wj−∑j=⌊n/2⌋+1iwj−∑j=i+1kwj>0.In formula (15), M is the constraint parameter of situational music, and n,w,c,r are negative, tired, rebellious, and tired of learning reaction parameters, and wj is a comprehensive and dispersive stressor and a social interpersonal stress parameter. Through piecewise regression analysis and statistical analysis, the evaluation and optimization of music therapy’s auxiliary effect on mental health can be realized. According to the differences between the experimental group and the control group in parents’ pressure, self-pressure, teachers’ pressure, social pressure, and study pressure, combined with the difference test and analysis of some data of stress release of music therapy on mental health, the quantitative evaluation of the auxiliary effect of music therapy on mental health was realized through artificial intelligence optimization control.


## 5. Empirical Analysis and Test

In order to verify the performance of the model in evaluating the auxiliary effect of music therapy on mental health, SPSS statistical analysis software was used to analyze the data, and the practice of this research course was conducted at a university. There are 66 teaching classes in colleges and universities, with more than 13,500 students and 1,309 faculty members, including 266 front-line teachers. The questionnaire consists of 21 questions in four aspects: parents’ pressure, self-pressure, teachers’ pressure, and social pressure. The original data collected by the questionnaire are input and summarized in Excel, and the data are compared and analyzed by professional statisticians. The data sampling length of the qualitative performance index evaluation of music therapy’s auxiliary effect on mental health is 1024, and the regression coefficient of internal control of music therapy’s auxiliary effect on mental health is 2.5. The KMO and spherical test of the learning stress scale are carried out, and the KMO value of the scale is 0.85,p=0.01<0.05. Therefore, the validity of the learning stress scale in this study is very good. The initial value of the regression analysis model is x0=2, y0=2, z0=2, and the correlation statistical analysis results are shown in Table 1.Table 1
Statistical analysis results of the correlation between music therapy and the evaluation of the auxiliary effect of mental health.
DatasetNumber of attributesFeature set scale/MBitStatistical dimensionDataset 143130005Dataset 236140008Dataset 3345400012Dataset 4561200054According to the statistical results in Table1, SPSS25.0 is used to analyze the data obtained from the questionnaire. Using the regression analysis method, the internal control and regression analysis of music therapy’s auxiliary effect on mental health was carried out, the related characteristic information of music therapy’s auxiliary effect on mental health was extracted, and the process optimization control method was used to evaluate music therapy’s auxiliary effect on mental health. According to the data objects in Table 1, the overall characteristics (N = 318) were obtained as shown in Table 2.Table 2
General characteristics (N = 318).
Project/scoreMinimum valueMaximumMean valueStandard deviationParental pressure0.4663.0315.2530.604Self-pressure0.4714.3024.2960.862Teacher pressure0.2062.3500.7400.106Social stress0.5053.6711.8590.670Learning pressure0.2823.5015.7500.758Taking gender as the independent variable and students’ learning pressure as the dependent variable, the independent samplet-test is conducted. The results are shown in Table 2: there are significant differences in students’ self-stress, social stress, and learning pressure, and the scores of girls are significantly higher than those of boys, while there is little gender difference in teachers’ stress. Taking class type as the independent variable and students’ learning pressure as the dependent variable, the independent sample t-test is conducted. The analysis results are shown in Figure 2. The analysis shows that there is a significant difference in self-stress in class type, and j is the score of the control class which is significantly higher than that of the experimental class.Figure 2
T-test results of independent samples for evaluation of music therapy’s auxiliary effect on mental health. (a) Test set. (b) Training set.
(a)(b)According to the regression analysis results of the evaluation of music therapy’s auxiliary effect on mental health (Figure2), it is found that the contribution of music therapy using this model to the evaluation of mental health’s auxiliary effect is high. The independent sample X-square test is carried out with the family location as the white variable and the student’s learning pressure as the dependent variable. The analysis results are shown in Figure 3. The analysis shows that there are significant differences in parental pressure, self-pressure, teacher pressure, and learning pressure in the family location, and the scores of high school students from cities are significantly higher than those from rural areas.Figure 3
Independent sample X-square test for the evaluation of the auxiliary effect of music therapy on mental health. (a) Test set. (b) training set.
(a)(b)According to the feedback data of the pretest questionnaire in Figure3, two classes were selected as the subjects of curriculum intervention. Set once a week, 45 minutes each time, for a total of 4 weeks, as a course cycle. The first course is a preliminary stage, which guides students to put aside all kinds of restrictions in class on weekdays, follow the guidance, relax, pay attention to their inner feelings and changes, and adapt to the link and speed of the music relaxation experience course without disturbing others. The second course is the adaptation stage. Students who participate in the music relaxation experience have corresponding experience expectations or psychological preparation when they arrive at the designated classroom. The third and fourth times are in-depth experience stages, in which students devote themselves to music relaxation and self-feeling exploration.At the end of the fourth experience course, we will give out the posttest questionnaire, collect the data, and make a comparative analysis of the data before and after the course, so as to obtain the course effect. We analyze the performance index evaluation feature distribution set of music therapy’s auxiliary effect on mental health, extract the related feature information of music therapy’s auxiliary effect on mental health, and get the evaluation confidence level as shown in Figure4.Figure 4
Confidence level of evaluation of music therapy’s auxiliary effect on mental health. (a) Test set. (b) Training set.
(a)(b)According to the analysis of Figure4, the accuracy of music therapy in this paper in assessing the auxiliary effect on mental health is high, which is more than 90%, while the literature methods are more than 90%. Under the evaluation of music amplitude, music therapy has a higher ability to assist mental health and a higher level of contribution to performance indicators. Using this method to evaluate music therapy to assist mental health has higher accuracy, better adaptability, higher confidence level, and accurate and reliable evaluation results, which improves the quantitative analysis ability of music therapy to assist mental health. Schools do not pay enough attention to music lessons. Usually, only music appreciation courses are offered in schools, with a single type of courses and rigid teaching methods, so that students are not interested and the courses cannot be carried out smoothly. Some students will do things unrelated to the course content in the music class, such as doing homework in other subjects and sleeping. In order to enable the course to continue, teachers can only play music-related movies and videos for students, and students call the music and beauty class “sleeping class” and “film class.” In addition, in the high school stage, some students have the learning experience of extracurricular art expertise, and the music quality among students is quite different. Some students learn the same course content easily, and some students start class by listening to gobbledygook, so it is difficult to achieve balanced and comprehensive teaching. Practical music skills courses are difficult to carry out, and all kinds of advanced equipment provided by the school can only be dusty. The lack of substantial music teaching for a long time leads to the students’ lack of basic music literacy.


## 6. Conclusions

In this paper, the performance index evaluation model of music therapy’s auxiliary effect on mental health is established, the informatization level of music therapy’s auxiliary effect on mental health is improved, and the evaluation model of music therapy’s auxiliary effect on mental health based on data sharing is put forward. This paper constructs the optimal fusion characteristic parameter analysis model for the evaluation of music therapy’s auxiliary effect on mental health, reconstructs the association rules of the evaluation data of music therapy’s auxiliary effect on mental health, analyzes the evaluation characteristic distribution set of performance indicators of music therapy’s auxiliary effect on mental health, extracts the related characteristic information of music therapy’s auxiliary effect on mental health, and adopts the process optimization control method to evaluate music therapy’s auxiliary effect on mental health. The research shows that this model can effectively improve the accuracy of the evaluation of music therapy’s auxiliary effect on mental health, and the evaluation results are accurate and reliable, with low error and good confidence.From the perspective of facing groups, in the music therapy experience, it is an important basic ability for participants to accurately capture the information contained in music materials. The more accurate the participants capture music information, the deeper the follow-up music therapy experience will be, and the more obvious the desired effect will be. Therefore, original music learning in the traditional music classroom can lay the foundation for students’ perception and comprehension of music materials. Secondly, as students, the time spent in school occupies most of their lives, and school is a small society for students. They will have pressure everywhere when they deal with their studies, teachers, and classmates in school, so all kinds of psychological discomfort will inevitably occur. However, students’ ability to adjust their own psychology is limited, and it is difficult for them to correctly face problems and pay attention to their inner world in the face of various pressures. Internally, their coping ability is limited, and externally, they are ashamed to seek help from teachers, parents, or others. Therefore, students’ demand for psychological counseling is very urgent. Thirdly, putting the application of music therapy in the music classroom fundamentally determines the comprehensiveness of this course, so that new experience forms and new course methods will no longer have only a small number of beneficiaries. From the point of view of the method itself, receptive music therapy emphasizes the related experience brought by listening, among which muscle gradual relaxation and guided music imagination method have simple operating conditions and have no special requirements for participants, so they are suitable for groups that need relaxation experience. Therefore, they can be based on the traditional music classroom, conduct a universal music relaxation experience for all students, and exert a subtle and positive influence on students’ psychology. The experience of music therapy and relaxation is also the further deepening of the music curriculum, which can reach the new requirements of music curriculum for students more profoundly. Music therapy and traditional music classroom teaching complement each other, opening up students’ new vision of music class and understanding another meaning of music. Finally, teachers’ learning receptive music therapy is not only a brand-new challenge for themselves but also a good opportunity to explore and enrich themselves.In a word, introducing receptive music therapy into middle school music classes can improve the deficiency of traditional middle school music classes and increase the richness of music classes. It can also expand the practicality of a music classroom, make music harmonious, make students develop healthily, and further deepen the people-oriented concept in education.



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*Source: 9960589-2022-09-23.xml*