# Cultural Value and Strategy of Ethnic Music Based on Distribution Estimation Algorithm

Authors: Junyan Shi; Qinliang Ning
Journal: International Transactions on Electrical Energy Systems
 (2022)

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## Abstract

The current social construction process is gradually accelerating, and people’s growing spiritual and cultural needs are eager to be satisfied. Spiritual culture is conceived from the material production process of human beings. It is a unique ideology of human beings that is different from other animals. National music culture is a branch of spiritual culture. It occupies an indispensable position in China, and its development is closely related to the inheritance of national culture. At present, the cultural value of ethnic music has not been fully excavated. The reason is that the research methods used are relatively backward, resulting in insufficient optimization of the analysis method. Therefore, this paper applied the distribution estimation algorithm and the information index method to the analysis of the cultural value and strategy of ethnic music. After simulating 120 samples, this paper obtained four results: convergence rate results, average optimal value results, sorting effect results, and final optimization degree results. The experimental results showed that the research method after applying the distribution estimation algorithm can improve the optimization degree of the value and strategy analysis of ethnic music culture by 5.81%, which is conducive to the better value of ethnic music culture in social spiritual and cultural construction.


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## Body

## 1. Introduction

In recent years, under the influence of the rapid development of popular music, ethnic music is in an awkward situation, resulting in fewer and fewer people who understand ethnic music. Under the general consensus, a nation with a long history can breed rich national music, which is the crystallization of the essence of culture. And, under this real situation, the analysis of the value and strategy of national music culture has positive practical significance for the construction of spiritual culture.There are some studies on ethnic music culture by scholars. Chan’s research considered the implementation of ethnic music education in the local area through a practice-oriented approach, mainly by offering ethnic music lessons in the classroom and playing ethnic music in public places to promote the sustainable development of local traditional culture [1]. The findings of Savage et al. suggested that even creative art forms like folk music are subject to evolutionary constraints similar to genetics, language, and other cultural domains. These constraints will restrict the development of folk music by leaps and bounds, resulting in a slow pace of development [2]. Sirek used the dual perspectives of world music education and national music education in his research to clarify the differences in the impact of different types of music education on people. This effect tends to accompany a person’s life, and different music may shape different personalities [3]. In order to analyze the feasibility of integrating ethnic music into children’s music teaching, Su and Jiang’s research proposed a teaching strategy system for children’s music knowledge based on case reasoning. The system collected sufficient data and information through questionnaires. Experimental research showed that most teachers have a positive attitude towards the development and utilization of ethnic music educational resources in kindergartens, but parents do not have a sufficient understanding of ethnic music [4]. Ali’s research examined the mediation of folk music, showing how a community uses folk music as an alternative form of media to voice its demands. The specific method is to use the metaphorical expressions in traditional folk music to describe my thoughts [5]. Dijana analyzed the cultural representation between folk music and national identity through historical research methods and comparative research methods [6]. Singh demonstrated the potential of folk music for understanding social, cultural, and economic trends and their spatial trends through a specific study of ethnic music in a region [7]. The research analyzed the relevant connotation and development of national music culture.In addition, many scholars have conducted research on distribution estimation algorithms. Yang et al. proposed a multimodal distribution estimation algorithm combined with a clustering strategy in their research, which has three unique techniques: dynamic adjustment of cluster size, distributed computing, and adaptive local search. The algorithm is very promising for complex problems with many local optima [8]. The research of Arenas et al. proposed a distribution estimation algorithm suitable for calculating the estimator of the innovation process. The algorithm adopts a macro-level evolution method based on the search space, with stronger global search ability and faster convergence speed [9]. The research of Irurozki et al. pointed out that the permutation problem is a combinatorial optimization problem, and proposed an optimization scheme. It is encoded as a sequence, which is convenient to deal with linear sorting problems and quadratic distribution problems [10]. Hedar et al. proposed a fuzzy logic-based sampling technique to deal with small sample sizes and designed a distribution estimation algorithm based on simulation optimization. It will be used to solve nonlinear continuous optimization problems involving noise, making the optimization more reasonable [11]. Aiming at the localization and tracking of multiple optimal values in a multimodal environment, Yu et al. proposed a distribution estimation algorithm based on incremental clustering. The main idea of this algorithm was to build multiple probability models based on incremental clustering, which improved the performance of locating multiple local optimal values and helped to quickly find the global optimal solution to dynamic multimodal problems [12]. Sudholt and Witt developed a probabilistic model-based distribution estimation algorithm by repeatedly sampling from the distribution and updating it based on the existing samples. Thereby, a probability distribution that is beneficial to the optimal solution in the underlying search space is evolved [13]. Rodriguez and Aguirre improved the performance of the distribution estimation algorithm through the probability model and used the generalized distribution estimation algorithm to obtain a set of explicit probability distributions for solving line optimization problems [14]. The study by Fard and Mohaymany proposed that the application of explicit probability models based on Coppa helps distribution estimation algorithms to explore the search space more efficiently and offers the possibility to extract structure by analyzing probabilities. The results of the study showed that applying an explicit probability model based on Coppa helps distribution estimation algorithms to explore the search space more efficiently and provided the possibility to extract structure by analyzing probabilities [15]. From this, it can be seen that the application results of distributed estimation algorithms have been very rich.Nowadays, scholars have achieved some results in the research on ethnic music culture and distribution estimation algorithms and have provided a certain reference for others’ research at the theoretical level. But there is almost no precedent for combining the two to analyze the value and strategy of ethnic music culture. Therefore, this paper applied the distribution estimation algorithm to the analysis of the value and strategy of ethnic music culture, which is conducive to better inheritance and development of ethnic music culture, so that people’s spiritual and cultural needs can be further satisfied.


## 2. Value and Strategy of National Music Culture

### 2.1. Necessity of Carrying Out the Value and Strategy of Ethnic Music Culture

Music is the earliest art form, and ethnic music reflects the national customs and customs of the working people. Its biggest feature is that the past and the future are interrelated, and culture can be preserved in the form of songs in the span of time and space. It covers the essence of national music culture [16]. From the perspective of history and culture, folk music originated from the masses, produced a large number of spiritual and cultural characteristics in the development, and finally served the masses in the form of music. Ethnic music enriched people’s cultural life and effectively improved people’s spiritual and cultural life. With the further development of the times and culture, the folk music culture will gradually integrate from the unique regional culture to the vast world stage. Therefore, it is necessary and urgent to analyze the value and strategy of the folk music culture.


### 2.2. Value and Strategy System Structure of National Music Culture

National music culture embodies the great wisdom of all nations in the world. Different ethnic groups have created different spiritual wisdom and cultural products due to their different living environments and historical evolution factors. These cultural symbols of various ethnic groups and regions are the source of creating the national centripetal force and the embodiment of the national spirit. Ethnic music plays a very important role in the development of world music history. Through the research on the status, function, and value of the nation in the history of music development, providing a steady stream of national blood for the development of national music, and further developing its profound cultural value. The value and strategy analysis framework of ethnic music culture is shown in Figure1.Figure 1
Value and strategy framework of folk music culture.The analysis system improved in this paper takes the distribution estimation algorithm as the core, supplemented by the information indexing method, and will explore the theoretical properties of the algorithm aiming at the important underlying issues in the retrieval and evaluation criteria of ethnic music culture. This paper conducted an in-depth study of the mainstream by directly optimizing the evaluation criteria for information retrieval and studied the theory of direct optimization of criteria-based retrieval models for evaluating information retrieval, in order to gain an in-depth understanding of the theoretical nature of such models.Information retrieval is required before analyzing the value and strategy of ethnic music culture. Data retrieval refers to the process of obtaining data that meets user information needs from a large amount of unstructured data. The result of data retrieval will generate a data table, which can be put back into the database or used as an object for further processing [17]. Retrieval systems use special data structures called inverted indexes to store and organize unstructured data. When executing a query, the retrieval system parses the query words, creates a series of values, uses the created values to retrieve according to a specific retrieval strategy, and finally returns the retrieval results to the user. The information retrieval method is shown in Figure 2.Figure 2
Information retrieval.Before analyzing and indexing, users will have a demand for ethnic music and cultural information, and this information demand will lead to a query. User-submitted queries usually contain multiple terms related to ethnic music culture. In searches, the query usually consists of 2-3 terms. The terms here do not refer to words, because in some ethnic music contexts, terms are not words. The query words input by the user are usually short strings, and the retrieval system generally supports comprehensive query statements. Common query statements include complex Boolean expressions and pattern matching operators. These query syntaxes can restrict user queries to specific document fields or use filters to restrict queries to specific documents.The task of the search engine module is to process queries entered by users, maintain document datasets and handle inverted indexes. One of the most basic functions of reverse indexing is to provide the mapping relationship between document expressions and documents, which is the key to achieve relevance search and ranking [18].


## 2.1. Necessity of Carrying Out the Value and Strategy of Ethnic Music Culture

Music is the earliest art form, and ethnic music reflects the national customs and customs of the working people. Its biggest feature is that the past and the future are interrelated, and culture can be preserved in the form of songs in the span of time and space. It covers the essence of national music culture [16]. From the perspective of history and culture, folk music originated from the masses, produced a large number of spiritual and cultural characteristics in the development, and finally served the masses in the form of music. Ethnic music enriched people’s cultural life and effectively improved people’s spiritual and cultural life. With the further development of the times and culture, the folk music culture will gradually integrate from the unique regional culture to the vast world stage. Therefore, it is necessary and urgent to analyze the value and strategy of the folk music culture.


## 2.2. Value and Strategy System Structure of National Music Culture

National music culture embodies the great wisdom of all nations in the world. Different ethnic groups have created different spiritual wisdom and cultural products due to their different living environments and historical evolution factors. These cultural symbols of various ethnic groups and regions are the source of creating the national centripetal force and the embodiment of the national spirit. Ethnic music plays a very important role in the development of world music history. Through the research on the status, function, and value of the nation in the history of music development, providing a steady stream of national blood for the development of national music, and further developing its profound cultural value. The value and strategy analysis framework of ethnic music culture is shown in Figure1.Figure 1
Value and strategy framework of folk music culture.The analysis system improved in this paper takes the distribution estimation algorithm as the core, supplemented by the information indexing method, and will explore the theoretical properties of the algorithm aiming at the important underlying issues in the retrieval and evaluation criteria of ethnic music culture. This paper conducted an in-depth study of the mainstream by directly optimizing the evaluation criteria for information retrieval and studied the theory of direct optimization of criteria-based retrieval models for evaluating information retrieval, in order to gain an in-depth understanding of the theoretical nature of such models.Information retrieval is required before analyzing the value and strategy of ethnic music culture. Data retrieval refers to the process of obtaining data that meets user information needs from a large amount of unstructured data. The result of data retrieval will generate a data table, which can be put back into the database or used as an object for further processing [17]. Retrieval systems use special data structures called inverted indexes to store and organize unstructured data. When executing a query, the retrieval system parses the query words, creates a series of values, uses the created values to retrieve according to a specific retrieval strategy, and finally returns the retrieval results to the user. The information retrieval method is shown in Figure 2.Figure 2
Information retrieval.Before analyzing and indexing, users will have a demand for ethnic music and cultural information, and this information demand will lead to a query. User-submitted queries usually contain multiple terms related to ethnic music culture. In searches, the query usually consists of 2-3 terms. The terms here do not refer to words, because in some ethnic music contexts, terms are not words. The query words input by the user are usually short strings, and the retrieval system generally supports comprehensive query statements. Common query statements include complex Boolean expressions and pattern matching operators. These query syntaxes can restrict user queries to specific document fields or use filters to restrict queries to specific documents.The task of the search engine module is to process queries entered by users, maintain document datasets and handle inverted indexes. One of the most basic functions of reverse indexing is to provide the mapping relationship between document expressions and documents, which is the key to achieve relevance search and ranking [18].


## 3. Application of Distribution Estimation Algorithm in System

The origin of the proposed distribution estimation algorithm comes from the genetic algorithm. When genetic algorithms solve complex combinatorial optimization problems, they can usually obtain better optimization results faster than some conventional optimization algorithms [19]. The core phase of the algorithm is divided into three steps: selection, crossover, and mutation operations. Through the implementation of these three steps, the optimization of the cultural value and strategy analysis method of ethnic music is completed. The “pattern theorem” and the “building block hypothesis” are the VI. In this part of the text, “the root of the distribution estimation algorithm comes from the genetic algorithm,” which needs a brief and effective introduction to the genetic algorithm cornerstones of the algorithm. The algorithm is mainly to select, intersect and modify the building blocks, form better building blocks through the operation of these three processes, and obtain the optimal or optimal solution of the problem. However, in the process of the algorithm’s circular algorithm, there is randomness in cross-linking and mutation, which will affect and destroy the rearrangement of building blocks, thereby affecting the optimization performance of the algorithm. In order to solve the problem of destructive optimization, this paper used the distribution estimation algorithm to improve. The distribution estimation algorithm is an evolutionary algorithm based on probability analysis, and there are obvious differences in the evolution state of the distribution estimation algorithm. The distribution estimation algorithm mainly extracts the individual structure information of the dominant group, establishes a probability model describing the solution space, samples the new generation group from it, and iterates through the optimization process. The distribution estimation algorithm effectively eliminated the possible early convergence dilemma in the algorithm. The structure diagram of the distribution estimation algorithm is shown in Figure 3.Figure 3
Estimation of distribution algorithms structure diagram.Distribution estimation algorithms use statistical learning methods to evaluate the population probability distribution and generate dominant details based on the probability distribution to achieve population evolution [20]. It is evolved on the basis of a traditional genetic algorithm. However, population development is no longer based on traditional genetic operations such as crossover, mutation, and combination, but based on probability models derived from statistical learning to generate new details.The realization process is to first initialize the population and create multiple details to build a complete population, use the fitness value as a criterion to measure details, select excellent details to create a dominant group, extract information from the dominant population and estimate the probability distribution of the dominant population using statistical learning methods. A random sample based on this distribution constitutes the next generation of the population, and so on until the termination condition is met. The distribution evaluation process, like other intelligent algorithms, should include three parts. They are the initialization of the population, the definition of evolutionary rules, and the control of termination conditions. Initialization of the population is the most important step. It will determine whether the entire algorithm is successful or not. The definition of evolutionary rules should be adjusted according to the actual situation. And, the control of termination conditions is the key to the entire process and determines the final result. The definition of the entire evolution mainly includes fitness value evaluation, probability distribution update rules, random sampling, and so on. Depending on the complexity of the probabilistic model and the different ways of choosing strategies, different applications of distribution estimation algorithms can be created.


## 4. Algorithm Processing in the System

### 4.1. Selection Strategy

The core of the distribution estimation algorithm is the construction of a probability model based on the dominant population, and the dominant population selection method plays a crucial role in the composition of the probability model [21]. The basic steps of the distribution estimation algorithm can be expressed as follows.Choose(1)px,t⟶psx,t.Build the probability distribution(2)psx,t⟶px,t+1.The proportional selection strategy is the most common selection method in evolutionary algorithms [22]. In proportional selection, the probability of an individual being selected as the parent is proportional to its fitness value, namely,(3)psx,t=fxpx,tEt.Among them,px,t is the average fitness value. Et is the dependence coefficient of the update process of the probability distribution under the proportional selection strategy.Next, the operation of truncation selection needs to be performed. First, the obtained individuals are sorted according to the fitness value, and then the better individuals are selected according to the truncation coefficient:(4)psx,t=px,tαt.Among them,(5)αt=∫fx≥βtpx,tdx,where βt is a real number, which can be a constant independent of t, that is, only individuals whose fitness value is not less than βt can be selected as the parent.


### 4.2. Bivariate Correlation Distribution Estimation Algorithm

In practical problems, the situation where variables are not correlated is limited. Generally speaking, there is a certain correlation between variables [23]. With the introduction of correlation, the parameters also increase, the calculation process will change greatly in time and space, and approximate population probability models will become more complex. To simplify the investigation process, it is assumed that there is only one correlation between two variables, that is, a bivariate correlation type distribution estimation algorithm. Its probability model is shown in Figure 4.Figure 4
Probabilistic model.The algorithm describes the relationship between variables through a forest structure. The correlation results of a parent node with other nodes are selected and calculated randomly, and the node with the highest correlation to the graph is added. Continuing to loop this process until there is no correlation between the nodes. The probability model of the algorithm is as follows:(6)pgx=∏xr∈Rgpgxr∏xr∈V\Rgpgxr|xji,where pg is the root node in the g generation forest structure, V is the full node, and xji is the parent node of xi.The probability distribution of the solution space can be expressed as follows:(7)px=px1,x2,…,xn=∏i=1npxi.The formula for the updated probability vector of the dominant group is as follows:(8)pl+1x=1−αplx=α1N∑k=1Nxlk,where pl+1x represents the probability vector of the l+1 generation. plx represents the probability vector of the l generation. xlk represents the individuals in the dominant group of the l generation, N represents the group size. α represents the learning rate.


### 4.3. Binomial Distribution

The binomial distribution is an important distribution introduced by then-fold Bernoulli test [24]. For n-fold Bernoulli trial, if the probability of observing an event in each trial is p, then the random variable used to describe the number of events observed in n trials obeys a bivariate parameter of (n,p) item distribution. The binomial distribution can be described as follows:(9)pX=k=Cnkpk1−pn−k.Assuming that there arem individuals in the dominant group in the problem, and each individual contains n variables, then these n variables correspond to n positions, and the individuals in the dominant group are as follows:(10)x1=Xi111,Xi221,…,Xinn1x2=Xi112,Xi222,…,Xinn2xm=Xi11m,Xi22m,…,Xinnm.For each position of all individuals in the dominant group, the number of occurrences of each variable in the individual in all possible positions is counted, and a quantity matrixK is obtained, the matrix is as follows:(11)K=k11k12⋯k1nk21k22⋯k2n⋮⋮⋱⋮kn1kn2⋯knn.Each column of the quantity matrixK corresponds to a position, n columns correspond to n positions, each row corresponds to a variable, and n rows correspond to n variables in an individual.Its binomial probability is calculated as follows:(12)pij=Cmkijpkij1−pm−kij.Thereby, the corresponding binomial distribution probability model is established:(13)P=p11p12⋯p1np21p22⋯p2n⋮⋮⋱⋮pn1pn2⋯pnn.The formula for calculating the binomial probability is as follows:(14)pijt=CDkijt1nkijt1−1nD−kijt,where CDkijt is the expected likelihood of a probabilistic occurrence.Thus, the corresponding binomial probability model is established asPt:(15)P=p11tp12t⋯p1ntp21tp22t⋯p2nt⋮⋮⋱⋮pn1tpn2t⋯pnnt.To achieve the update of the binomial probability model, the update method is(16)pijt+1=βpijt+1−βhijt+1.Then there is a functionC that is(17)Hx,y=CFx,Gy,hijt+1 is the new binomial probability, pijt+1 is the binomial probability model.There exists anN-dimensional function C that is(18)Hx1,x2,…,xN=CF1x1,F2x2,…,FNxN.This measure is the second-order difference of the function on the rectangle, namely,(19)Vca,b×c,d=ΔabΔcdCu,v.Then, the function determined byF is(20)Cu1,…,un=FF1−1u1,…,Fn−1un.


## 4.1. Selection Strategy

The core of the distribution estimation algorithm is the construction of a probability model based on the dominant population, and the dominant population selection method plays a crucial role in the composition of the probability model [21]. The basic steps of the distribution estimation algorithm can be expressed as follows.Choose(1)px,t⟶psx,t.Build the probability distribution(2)psx,t⟶px,t+1.The proportional selection strategy is the most common selection method in evolutionary algorithms [22]. In proportional selection, the probability of an individual being selected as the parent is proportional to its fitness value, namely,(3)psx,t=fxpx,tEt.Among them,px,t is the average fitness value. Et is the dependence coefficient of the update process of the probability distribution under the proportional selection strategy.Next, the operation of truncation selection needs to be performed. First, the obtained individuals are sorted according to the fitness value, and then the better individuals are selected according to the truncation coefficient:(4)psx,t=px,tαt.Among them,(5)αt=∫fx≥βtpx,tdx,where βt is a real number, which can be a constant independent of t, that is, only individuals whose fitness value is not less than βt can be selected as the parent.


## 4.2. Bivariate Correlation Distribution Estimation Algorithm

In practical problems, the situation where variables are not correlated is limited. Generally speaking, there is a certain correlation between variables [23]. With the introduction of correlation, the parameters also increase, the calculation process will change greatly in time and space, and approximate population probability models will become more complex. To simplify the investigation process, it is assumed that there is only one correlation between two variables, that is, a bivariate correlation type distribution estimation algorithm. Its probability model is shown in Figure 4.Figure 4
Probabilistic model.The algorithm describes the relationship between variables through a forest structure. The correlation results of a parent node with other nodes are selected and calculated randomly, and the node with the highest correlation to the graph is added. Continuing to loop this process until there is no correlation between the nodes. The probability model of the algorithm is as follows:(6)pgx=∏xr∈Rgpgxr∏xr∈V\Rgpgxr|xji,where pg is the root node in the g generation forest structure, V is the full node, and xji is the parent node of xi.The probability distribution of the solution space can be expressed as follows:(7)px=px1,x2,…,xn=∏i=1npxi.The formula for the updated probability vector of the dominant group is as follows:(8)pl+1x=1−αplx=α1N∑k=1Nxlk,where pl+1x represents the probability vector of the l+1 generation. plx represents the probability vector of the l generation. xlk represents the individuals in the dominant group of the l generation, N represents the group size. α represents the learning rate.


## 4.3. Binomial Distribution

The binomial distribution is an important distribution introduced by then-fold Bernoulli test [24]. For n-fold Bernoulli trial, if the probability of observing an event in each trial is p, then the random variable used to describe the number of events observed in n trials obeys a bivariate parameter of (n,p) item distribution. The binomial distribution can be described as follows:(9)pX=k=Cnkpk1−pn−k.Assuming that there arem individuals in the dominant group in the problem, and each individual contains n variables, then these n variables correspond to n positions, and the individuals in the dominant group are as follows:(10)x1=Xi111,Xi221,…,Xinn1x2=Xi112,Xi222,…,Xinn2xm=Xi11m,Xi22m,…,Xinnm.For each position of all individuals in the dominant group, the number of occurrences of each variable in the individual in all possible positions is counted, and a quantity matrixK is obtained, the matrix is as follows:(11)K=k11k12⋯k1nk21k22⋯k2n⋮⋮⋱⋮kn1kn2⋯knn.Each column of the quantity matrixK corresponds to a position, n columns correspond to n positions, each row corresponds to a variable, and n rows correspond to n variables in an individual.Its binomial probability is calculated as follows:(12)pij=Cmkijpkij1−pm−kij.Thereby, the corresponding binomial distribution probability model is established:(13)P=p11p12⋯p1np21p22⋯p2n⋮⋮⋱⋮pn1pn2⋯pnn.The formula for calculating the binomial probability is as follows:(14)pijt=CDkijt1nkijt1−1nD−kijt,where CDkijt is the expected likelihood of a probabilistic occurrence.Thus, the corresponding binomial probability model is established asPt:(15)P=p11tp12t⋯p1ntp21tp22t⋯p2nt⋮⋮⋱⋮pn1tpn2t⋯pnnt.To achieve the update of the binomial probability model, the update method is(16)pijt+1=βpijt+1−βhijt+1.Then there is a functionC that is(17)Hx,y=CFx,Gy,hijt+1 is the new binomial probability, pijt+1 is the binomial probability model.There exists anN-dimensional function C that is(18)Hx1,x2,…,xN=CF1x1,F2x2,…,FNxN.This measure is the second-order difference of the function on the rectangle, namely,(19)Vca,b×c,d=ΔabΔcdCu,v.Then, the function determined byF is(20)Cu1,…,un=FF1−1u1,…,Fn−1un.


## 5. Comparison of Experimental Results for Methods

The comparison of experimental results in this paper is based on statistical analysis. In order to test the difference of the comparison results, the statistical analysis method used in this paper is the Wilcoxon signed-rank test method. This method adds the rank of the absolute value of the difference between the observed value and the center position of the null hypothesis as its test statistic with different signs, which is used to verify whether the improved analysis method is significantly better than the unimproved analysis method. The improved analysis method is denoted as A1, and the unimproved analysis method is denoted as A2. In each of the 20 rounds of independent operation using the distribution estimation algorithm, the three experimental results were recorded as samples, with a total of 120 samples.


### 5.1. Comparison of Convergence Speed Results

In numerical analysis, the speed at which a convergent sequence approaches its limit is called the convergence rate. This concept is mainly used in optimization algorithms. Convergence is defined as the rate at which an iterative sequence approaches its local optimum (assuming the computation process converges and reaches the optimum), and is an important indicator for evaluating the performance of an iterative method on the problem. Applying the convergence rate index to the experiment can reflect the optimization degree of the two analysis methods measured to a certain extent. The faster the convergence, the more optimized the analysis. The comparison of the convergence rate results of the two analysis methods is shown in Figure5.Figure 5
Convergence speed comparison.Figure5 shows the comparison of the results of the two analysis methods in terms of the convergence speed. It can be seen that the convergence speed of the improved analysis method is in the range of 0.2 S–0.4 S, and the volatility is small. However, the convergence speed of the unimproved analysis method is slow, in the interval of 0.2 S–0.8 S, and the volatility is large. In contrast, the convergence speed of the two is quite different, and their influence on the optimization degree of the analysis method is also very different.


### 5.2. Comparison of Average Optimal Value Results

In this paper, the comparison of the average optimal values of the two analysis methods under different rounds is given, and the average optimal values of the two analysis methods obtained from the experiments are compared with the known optimal values of the function. The known optimal value of the function is a fixed value, which is the best value obtained after testing. The larger the deviation between the average optimal value of the analysis method and the known optimal value of the function, the worse the optimization degree. And, the smaller the deviation value, the better the optimization degree. The comparison results of the average optimal value of the two analysis methods and the known optimal value of the function are shown in Figure6.Figure 6
Bias contrast.Figure6 reflects the comparison of the average optimal values of the two analysis methods under different rounds. It can be seen that the difference between the average optimal value of the improved analysis method and the known optimal value of the function is smaller in the vertical axis value. It means that the deviation value is small. However, the difference between the average optimal value of the unimproved analysis method and the known optimal value of the function is relatively large, indicating that the difference is large. It can be concluded that the improved analysis method has a more matched average optimal value, which had a positive effect on the optimization degree of the analysis method.


### 5.3. Comparison of Sorting Effect Results

In this paper, the comparison of the sorting effects of the two analysis methods under different rounds is given, and the sorting effects of the two analysis methods obtained by the experiment are compared. Sorting is the process of matching a set of “unordered” sequence records with “ordered” sequence records. If the entire sorting process can be completed without accessing the external memory, this sorting problem is called internal sorting. On the contrary, if the number of records involved in sorting is large, the sorting process of the entire sequence in memory alone cannot be completed. And, to complete the sorting by accessing the external storage method, then this sorting problem is called external sorting. Sorting stability means that if the relative position of the items with the same keyword in the original record sequence remains unchanged after sorting, the sorting effect is stable. If the relevant position changes, the sorting effect is not stable enough. The sorting effect is related to the degree of optimization. The more accurate the sorting effect, the better the degree of optimization. The comparison results of the sorting effects of the two analysis methods are shown in Figure7.Figure 7
Sorting effect comparison.Figure7 reflects the comparison of the ranking effects of the two analysis methods under different rounds. It can be seen that the improved analysis method has a ranking accuracy range of 0.5–0.9, while the unimproved analysis method has a ranking accuracy range of 0.2-between 0.8. And, it can be concluded that the improved analysis method has higher ranking accuracy.


### 5.4. Comparison of Final Optimization Results

The three comparison results obtained from the experiments are comprehensively weighted, and the weight determination method used in this paper is the fuzzy comprehensive evaluation and decision-making method. This is an approximate method of assigning weights, which combines the influence of different factors on the result, judges the evaluation level to form a fuzzy array, calculates the degree of attribution among the factors, and finally obtains the weight coefficient value of each factor by synthesizing the fuzzy matrix.Finally, the optimization results of the two analysis methods are obtained as shown in Figure8.Figure 8
Optimization comparison.Figure8 reflects that the improved analysis method has an optimization score of about 0.91, and the unimproved analysis method has an optimization score of about 0.86. Compared with the unimproved analysis method, the improved analysis method has an improvement of about 5.81% in optimization. Experiments showed that the improved analysis method has better application value.


## 5.1. Comparison of Convergence Speed Results

In numerical analysis, the speed at which a convergent sequence approaches its limit is called the convergence rate. This concept is mainly used in optimization algorithms. Convergence is defined as the rate at which an iterative sequence approaches its local optimum (assuming the computation process converges and reaches the optimum), and is an important indicator for evaluating the performance of an iterative method on the problem. Applying the convergence rate index to the experiment can reflect the optimization degree of the two analysis methods measured to a certain extent. The faster the convergence, the more optimized the analysis. The comparison of the convergence rate results of the two analysis methods is shown in Figure5.Figure 5
Convergence speed comparison.Figure5 shows the comparison of the results of the two analysis methods in terms of the convergence speed. It can be seen that the convergence speed of the improved analysis method is in the range of 0.2 S–0.4 S, and the volatility is small. However, the convergence speed of the unimproved analysis method is slow, in the interval of 0.2 S–0.8 S, and the volatility is large. In contrast, the convergence speed of the two is quite different, and their influence on the optimization degree of the analysis method is also very different.


## 5.2. Comparison of Average Optimal Value Results

In this paper, the comparison of the average optimal values of the two analysis methods under different rounds is given, and the average optimal values of the two analysis methods obtained from the experiments are compared with the known optimal values of the function. The known optimal value of the function is a fixed value, which is the best value obtained after testing. The larger the deviation between the average optimal value of the analysis method and the known optimal value of the function, the worse the optimization degree. And, the smaller the deviation value, the better the optimization degree. The comparison results of the average optimal value of the two analysis methods and the known optimal value of the function are shown in Figure6.Figure 6
Bias contrast.Figure6 reflects the comparison of the average optimal values of the two analysis methods under different rounds. It can be seen that the difference between the average optimal value of the improved analysis method and the known optimal value of the function is smaller in the vertical axis value. It means that the deviation value is small. However, the difference between the average optimal value of the unimproved analysis method and the known optimal value of the function is relatively large, indicating that the difference is large. It can be concluded that the improved analysis method has a more matched average optimal value, which had a positive effect on the optimization degree of the analysis method.


## 5.3. Comparison of Sorting Effect Results

In this paper, the comparison of the sorting effects of the two analysis methods under different rounds is given, and the sorting effects of the two analysis methods obtained by the experiment are compared. Sorting is the process of matching a set of “unordered” sequence records with “ordered” sequence records. If the entire sorting process can be completed without accessing the external memory, this sorting problem is called internal sorting. On the contrary, if the number of records involved in sorting is large, the sorting process of the entire sequence in memory alone cannot be completed. And, to complete the sorting by accessing the external storage method, then this sorting problem is called external sorting. Sorting stability means that if the relative position of the items with the same keyword in the original record sequence remains unchanged after sorting, the sorting effect is stable. If the relevant position changes, the sorting effect is not stable enough. The sorting effect is related to the degree of optimization. The more accurate the sorting effect, the better the degree of optimization. The comparison results of the sorting effects of the two analysis methods are shown in Figure7.Figure 7
Sorting effect comparison.Figure7 reflects the comparison of the ranking effects of the two analysis methods under different rounds. It can be seen that the improved analysis method has a ranking accuracy range of 0.5–0.9, while the unimproved analysis method has a ranking accuracy range of 0.2-between 0.8. And, it can be concluded that the improved analysis method has higher ranking accuracy.


## 5.4. Comparison of Final Optimization Results

The three comparison results obtained from the experiments are comprehensively weighted, and the weight determination method used in this paper is the fuzzy comprehensive evaluation and decision-making method. This is an approximate method of assigning weights, which combines the influence of different factors on the result, judges the evaluation level to form a fuzzy array, calculates the degree of attribution among the factors, and finally obtains the weight coefficient value of each factor by synthesizing the fuzzy matrix.Finally, the optimization results of the two analysis methods are obtained as shown in Figure8.Figure 8
Optimization comparison.Figure8 reflects that the improved analysis method has an optimization score of about 0.91, and the unimproved analysis method has an optimization score of about 0.86. Compared with the unimproved analysis method, the improved analysis method has an improvement of about 5.81% in optimization. Experiments showed that the improved analysis method has better application value.


## 6. Conclusion

In modern society, folk music is irreplaceable. They are the crystallization of cultural essence and have a positive effect on the spiritual and cultural construction of human society. In this reality, the analysis of the value and strategy of national music culture has positive practical significance. This paper applied the distribution estimation algorithm and the information indexing method to the analysis of the cultural value and strategy of ethnic music, in order to solve the problem that the cultural value of ethnic music has not been fully tapped. After simulating 120 samples, this paper obtained four experimental results. The experimental results showed that the research method after applying the distribution estimation algorithm can improve the optimization degree of the value and strategy analysis of ethnic music culture by 5.81%, which is conducive to the better value of ethnic music culture in social spiritual and cultural construction.



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*Source: 4160240-2022-09-19.xml*