# Problems and Solutions of Art Professional Service Rural Revitalization Strategy Based on Random Forest Algorithm

Authors: Jianhui Liang
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

The rural revitalization strategy proposal is not a passive water, but rather one with its own internal historical logic. The rural revitalization strategy has been implemented in a consistent manner with China’s rural construction practice over the last 100 years. Since modern times, the Chinese agricultural civilization has been severely harmed by the collision and friction with western industrial and commercial civilizations, which has directly aroused the strong reflection of people with lofty ideals from all walks of life in Chinese society and led them down the path of rural construction aimed at “rejuvenating” the countryside. Accelerating the development of rural culture is beneficial to the overall improvement of social culture and the happiness of the Chinese people. As a result, the development and construction of rural culture will have a significant impact on China. The random forest algorithm simulates the data learning process based on data mining, creates a model, and returns a judgment result. The strategy of art professional service is examined in this study. The random forest algorithm was used to revitalize rural areas. The random forest algorithm’s learning process is very quick, and it is still very effective in dealing with art professional services. The current random forest algorithm weighs the importance of all variables to determine the significance of switching from independent to dependent variables.


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## Body

## 1. Introduction

Rural revitalization strategy is the goal put forward by the 19th National Congress and the direction and guidance of China’s future development. The proposal of rural revitalization is another theoretical innovation and contribution of the party’s governance of the country, and it is also the result of the continuous promotion of the great practice of socialism with Chinese characteristics [1]. Professional services for rural revitalization strategy, the innovative advantage of business entities, lie in their ability to accurately grasp the supply information of agricultural products market and sell agricultural products through various marketing means. The proposal of rural revitalization strategy is not passive water, but has its inherent historical logic. Implementing the strategy of rural revitalization China’s rural construction practice in the past hundred years comes down in one continuous line. Since modern times, Chinese farming civilization has been severely hit by the collision and friction with western industrial and commercial civilization, which directly aroused the strong reflection of people with lofty ideals from all walks of life in China, and made them embark on the road of rural construction aimed at “reviving” the countryside. The deepest strength to realize rural revitalization in the new era lies in China’s excellent traditional culture. Furthermore, it is necessary to carry forward the excellent traditional culture of rural areas and enhance the self-confidence of rural culture [2]. The content of the strategy of rural revitalization and development is to study the development of rural construction by means of urban resources transmission and talent cultivation in colleges and universities and to achieve the sustainable development of rural culture, economy, spirit, and other construction directions. There are five general requirements for implementing the strategy of art major serving rural revitalization, and under the general requirements, it is subdivided into three stages of objectives and tasks. The existing literature related research at home and abroad mainly focuses on the information service strategy, mode, system, platform, and information transfer of the art major serving the rural revitalization strategy, but there is little research on the information service model of collaborative innovation in industry-university-research [3]. Collaborative innovation of professional service agriculture in industry-university-research is a process of working with new-type agricultural management entities, research institutes, and the government, using all kinds of information resources to research out the strategic scientific research achievements of rural revitalization and turn the scientific research achievements into real productive forces. Therefore, the rural revitalization strategy of art specialty serving agricultural industry-university-research collaborative innovation needs the support of various information resources and information services at each stage [4]. The object of art specialty service agricultural industry-university-research collaborative innovation information service refers to the demanders of rural revitalization strategic information service, including agricultural industry-university-research collaborative innovation teams composed of universities, scientific research institutes, new agricultural business subjects, and government departments. The information service subjects of art specialty serving the strategic innovation of agricultural industry-university-research cooperation for rural revitalization generally include libraries, scientific and technological information institutes of scientific research institutes, information institutions of new agricultural business entities, government information departments, and information intermediaries.Accelerating the construction of rural culture is beneficial to the overall promotion of social culture and people’s happiness, so the construction and development of rural culture will have a great impact on China. The new art major serves the strategic problem of rural revitalization [5, 6]. The purpose of participating in collaborative innovation of agricultural industry-university-research in colleges and universities is to obtain scientific research results, improve the quality of agricultural products, or develop new agricultural products by using scientific research results [7]. This determines that in the collaborative innovation of agricultural industry-university-research, the new art major is willing to cooperate with universities to serve the strategic problem of rural revitalization. To this end, the government should introduce relevant policies to encourage new-type agricultural business entities to raise funds through various channels, so that new-type art majors can serve the strategic problems of rural revitalization and business entities have the opportunity to turn the scientific research achievements of universities into productive forces [8, 9].Machine learning has gained a lot of traction in recent years all over the world. Random forest is used in all walks of life as a classifier with multiple decision trees established in a random manner, with the advantages of fast training speed, strong model generalization ability, and simple implementation when processing high-dimensional data [10]. A random forest is a machine learning algorithm that uses multiple decision trees to learn. It employs two randomization processes, resulting in classification trees with varying classification capabilities. The output results of random forest are voted by the classification results of each decision tree when the samples to be classified are input. Because of the various aspects of professional art service, many of the data and information collected for the rural revitalization strategy are not directly related, and much of the information are implicit. As a result, simplify the data, remove any redundant or useless information, and extract any potentially valuable data. The use of machine learning in rural revitalization strategies is therefore critical [11, 12]. It is discovered that the art major serving the rural revitalization strategy embodies the distinctive characteristics of nationality, inclusiveness, and long-term through comprehensive analysis and overall grasp of the general requirements, objectives, and tasks.The random forest algorithm is a process of simulating the data learning process based on data mining, establishing a model through the algorithm, and giving a judgment result [13]. This study mainly aims at diabetes to build an early warning model, that is, the diabetes early warning model. The randomness of the forest art professional service rural revitalization strategy is reflected in the random selection of samples and attributes. With these two factors, even if each tree is not pruned, there will be no fitting [14]. The self-help sample set is generated during the training process by sampling data from the art major service rural revitalization strategy each time. The self-help sample set will contain about 37% of the total sample set’s samples. Out-of-pocket data are what we call the 37% sample data. The random forest has improved prediction accuracy on the premise that the art major serving the rural revitalization strategy has not been significantly improved, and it is insensitive to multicollinearity, robust to missing data and unbalanced data, and can accurately predict the effects of thousands of variables [15]. When dealing with art professional services, the random forest algorithm’s learning process is very fast, and it is still very efficient. The current random forest algorithm weighs the importance of all variables and determines the significance of independent variable changes on the dependent variable. Many machine learning models have been compared at this time (Kampichler C, 2010; Li XH, 2013), and it has been discovered that the random forest algorithm performs better.


## 2. Related Work

According to literature [16], the implementation of an art specialty strategy for rural revitalization benefits from the political advantages of the party’s leadership, the benefits of the socialist system, the creative spirit of farmers, strong economic strength, long agricultural civilization, strong market demand, and other conditions and capabilities. With the continuous enrichment of material life and the increasing diversification of people’s needs, it is difficult to meet the daily needs of the people solely by relying on the government’s art professional service. In literature [17] through the big data analysis method, with the continuous enrichment of material life and the increasing diversification of people’s needs, it is difficult to meet the daily needs of the people only by relying on the government’s art professional service. China is in the “middle and late stages of industrialization, rapid development of informatization, middle stage of urbanization, and comprehensive promotion of agricultural modernization,” according to literature [18] research and has entered the stage of promoting art specialty to serve rural development. According to literature [19], the art professional service, the rural revitalization strategy is the new era’s overall strategy for the “three rural” issues. It is critical to break the iron law of rural decline, deepen the law of urban and rural development, and break the urban-rural dual pattern in order to resolve the major contradictions of the new era to build a socialist modern power, to break the iron law of rural decline, to deepen the law of urban and rural development, and to break the urban-rural dual pattern. The art professional service, according to the literature [20], uses the big data analysis method. Rural revitalization strategy is also evolving and improving over time. In this regard, academics have conducted research in order to establish and improve the art professional service rural revitalization system, add bricks and tiles to the construction of spiritual civilization in rural areas, and assist with rural revitalization implementation. Strengthening the strategic system of art professional service for rural revitalization, according to literature [21], is also an important way to meet the pursuit of a better life for rural residents. Literature [19] pointed out that the implementation of the strategy of art professional service for rural revitalization is not only the practical need to solve the main social contradictions in China at the present stage but also because China already has the practical conditions to start the strategy of rural revitalization. Literature [22] is through the big data analysis method; the research of literature [23] shows that art specialty serving rural revitalization is an inevitable requirement for realizing the great rejuvenation of the Chinese nation. At the same time, art professional service for rural revitalization is not only the need to plant the foundation of Chinese civilization and develop modern civilization but also the need and condition to realize China’s balanced, sustainable, and full development. On the other hand, research is on the significance of implementing rural revitalization strategy. Literature [24] proposes to enrich rural cultural life, adheres to the dominant position of rural residents, and protects the basic rights and interests of rural residents. At the same time, the plan also points out that we should increase the supply of rural public services and strengthen the construction of the rural art professional service system. The development of rural revitalization is inseparable from the construction of various security systems.Based on the random forest algorithm, this research studies the strategy of art majors serving rural revitalization. The related construction of rural revitalization cannot be separated from the exploration and utilization of traditional culture. In order to build a characteristic and sustainable rural culture, the protection and inheritance of rural culture must be done in the construction process.


## 3. Principle and Model of Random Forest Algorithm

The random forest classification algorithm is a classifier that trains and predicts samples by using multiple trees. Proposal for a professional art service rural revitalization action plan based on the random forest algorithm, the development of “agriculture, rural areas, and farmers,” is not a castle in the air, but rather has a solid practical foundation, which is primarily reflected in major achievements and accumulated work experience in the development of “agriculture, rural areas, and farmers.” The level of agricultural and rural development of Chinese art professional services has improved significantly under the condition that the party and the government place a high priority on agricultural and rural development. At the same time, the Engel coefficient of farmers has decreased significantly in recent years and is about to enter the affluent stage of less than 30%, which shows that farmers’ spending ability for development and enjoyment consumption is becoming stronger and stronger, and the economic conditions and social security level of farmers’ life have been effectively improved. It compensates the over fitting defect of decision tree by generating multiple decision trees and voting. Then, the random forest model is used to classify the feature vectors. The technical flow of this experiment is shown in Figure1.Figure 1
Technical route.The theoretical basis of the stochastic forest model is decision tree, which is obtained by combining decision trees, that is, randomizing the use of variables and data to generate many decision tree classification modelshX,θk,k=1,…, and there is no correlation between each tree. The strategic model of art professional service rural revitalization based on random forest is as follows.(i)
Have put back to random sampling. The random forest has a random choice to put back the samples in the training set.The selected number of samples is less than the total number of samples in the training set(ii)
Set the model parameters. In the random forest prediction model, the numbern of trees is taken as 100, and the number of n is selected as the basis. Through calculation, the node m is 4.(iii)
Model training. The model is trained by the samples selected in the training set, and the OOB of the model and the error rate of classification within the model are obtained.(iv)
Forecast. The random forest prediction model is used to classify and predict the test set, and the occurrence degree of the strategy of art major serving rural revitalization is obtained.The flowchart of the model is shown in Figure2.Figure 2
Flowchart of model construction.The basic idea of the random forest model is as follows: first, K samples are randomly selected from the original training set through bootstrap sampling, and the sample size of the sample is the same as that of the original training set. Second, the decision tree model is established for eachk samples, and the K classification results will be obtained; Finally, vote on each record according to the K classification results to determine the final classification, as shown in Figure 3.Figure 3
Flowchart of the random forest algorithm.In this study, the decomposition of the random forest algorithm and the Freeman decomposition are used to extract the polarization scattering characteristic parameters in the strategy of art majors serving rural revitalization. The eigenvalues/eigenvectors are used to decompose the coherence matrix. The form of the coherence matrix is as follows:(1)T3=∑i=13λi⋅μi⋅μi∗T,where λi I is the eigenvalue of the coherence matrix, and ∞≻λ1≥λ2≥λ3≥0,μi is the eigenvector corresponding to the eigenvalue.Using this decomposition, the following parameters can be obtained:(2)H=−∑i=13pilog3pi,α=∑i=13piαi,A=λ2−λ3λ2+λ3,λ=λ1+λ2+λ3.Among them,Pi=λi/∑n=13λn;λi is the eigenvalue of the coherence matrix, and αi and λi represent the scattering type of each pixel and the probability of occurrence of each scattering type, respectively. H is the scattering office, which represents the proportion of each scattering mechanism in the total scattering process and describes the randomness of the scattering process. A is the scattering angle, which represents the average scattering mechanism type. A is the degree of anisotropy, which describes the relationship between two weak scattering mechanisms in H/α decomposition. λ is the average scattering intensity, which contains the information that is not found in the scattering angle. This information can be used to distinguish the objects with similar scattering mechanism but different scattering intensities.It decomposes the covariance matrix into a linear combination of three scattering mechanisms, such as volume scattering, dihedral angle scattering, and surface scattering, as follows:(3)C3=fsC3surface+fdC3double+fvC3vol,where C3 surface corresponds to scattering, C3 double corresponds to dihedral angular scattering, C3 Vol represents the volume scattering. FS, Fd, and Fv correspond to the contribution of volume scattering component, dihedral angle scattering component, and surface scattering component, respectively. The corresponding power of the three scatterers is(4)Ps=fs1+β2,Pd=fd1+α2,Pv=83fv.


## 4. Strategic Problems and Solutions of Professional Services for Rural Revitalization

### 4.1. Strategy of Art Major Serving Rural Revitalization Based on Random Forest Algorithm

In the process of rural revitalization and construction, the demand for talents majoring in art and design is severe, and comprehensive personnel with solid design ability, professional knowledge, and certain traditional art foundation are needed. Of course, this demand also needs to be changed according to the special needs of regions, cultures, beliefs, and so on. In the strategic design of random forest art specialty serving rural revitalization, based on the completion of the overall and detailed design of the system, the specific coding realization of the strategy of art specialty serving rural revitalization based on the random forest algorithm is carried out. At present, the system has realized the functions of user registration, early warning analysis, and personal information management. There are five general requirements for implementing the strategy of art major serving rural revitalization, and under the general requirements, it is subdivided into three stages of objectives and tasks. Through comprehensive analysis and overall grasp of the general requirements, objectives, and tasks of the art major serving the rural revitalization strategy, it is found that it embodies the distinctive characteristics of nationality, inclusiveness, and long-term. The existing literature related research at home and abroad mainly focuses on the information service strategy, mode, system, platform, and information transfer of the art major serving the rural revitalization strategy, but there is little research on the information service model of collaborative innovation in industry-university-research. Under the background of art major serving rural revitalization strategy, the knowledge transfer of agricultural industry-university-research collaborative innovation refers to the transfer of knowledge capital to new agricultural management entities. Therefore, from the perspective of knowledge transfer, according to the diversified information service mode of agricultural industry-university-research collaborative innovation, this study makes an in-depth discussion and research on the information service model of art major serving rural revitalization strategy. After completing the relevant functions of the system, test and verify the functions of art professional service rural revitalization strategy and further improve the functions of art professional service rural revitalization strategy according to the test results. Many colleges and universities believe that rural construction is not easy to achieve results. Both capital investment and talent investment are not in direct proportion to the results. The results are slow, which is a waste of resources. If these resources are invested in other teaching and research, the benefits will be greater. The fusion methods of the random forest algorithm are diverse, and how to choose is based on the actual situation of the countryside. As a long-term development project, rural revitalization and construction must be guaranteed in terms of funds, not only in the process of construction but also in the training of art design major in new rural construction in colleges and universities. Many of them do not have direct funds, and the required expenses come from teaching funds, which is a very realistic and obvious problem for colleges and universities. Colleges and universities have great pressure on this project. If this problem is not solved, the investment of teachers will always be a serious problem. In the rural revitalization construction, we should pay attention to the protection of local intangible cultural heritage and historical and cultural buildings, which will require a large number of art talents. Therefore, the transportation of such talents is the responsibility of higher vocational colleges, and the introduction of such talents is the responsibility of the government.Based on the random forest algorithm, talents who meet the requirements of art majors to serve the rural culture construction can serve the rural revitalization construction as a disseminator of the rural revitalization strategic culture, allowing them to put all of their knowledge to good use and promote the rural culture construction. Rural culture must be revitalized in order to fully arouse people’s enthusiasm, and students majoring in art and design should use their professional expertise to create a new rural culture with distinct characteristics. Industry prosperity is the economic foundation for implementing the art major serving rural revitalization strategy. In contrast to the requirement of “production development” in the construction of new socialist countryside, production development refers to the improvement and optimization of agricultural production structure, development mode, and organic composition. The development of the existing strategy of art major serving rural revitalization is based on the basic conditions of agricultural production. It focuses more on improving the degree of integration between agricultural production and secondary and tertiary industries, so that farmers, as market participants, can share the value-added benefits of industrial development in the integration of tertiary industries and strive for “people and production prosperity.” Finally, the coordinated development of the agricultural system and nonagricultural industry system will realize the revitalization of art major serving rural industries. Students are urged to actively participate in the rural revitalization construction, go into the countryside, take root in the countryside, make their own contributions to the rural revitalization strategy, and reflect their sense of social responsibility as contemporary college students. The strategy of art specialty serving the rural revitalization should take the rural cultural construction as one of the courses, carry out special learning, carry out in-depth learning in combination with the rural revitalization strategy, and import professional talents into the rural revitalization construction. On this basis, I believe that with the active cooperation of colleges and universities, the construction and development of villages and villages will be very rapid. The results will be more obvious and easier to achieve. The school has limited teachers, and rural construction will consume teachers. However, this investment should be made. Rural construction is a necessary process for social development. At present, China’s rural cultural revitalization and construction institutions are mostly county level or village level cultural base stations. The characteristics of regional cultural stations are often characterized by low quality and few personnel, which seriously limit the distance of rural cultural construction. Therefore, it is very important for colleges and universities to serve rural cultural construction. It is necessary to build and continuously optimize the modern agricultural system and provide external conditions for the development of China’s rural revitalization strategic agricultural modernization through measures such as improving the scientific and technological content of art specialty service agriculture and improving the art specialty service agricultural protection and service system.


### 4.2. Experimental Results and Analysis

Collaborative modes of agricultural management entities, collaborative modes of research institutes, collaborative modes of government departments, and other collaborative innovation modes of professional service agriculture in industry-university-research include the collaborative mode, collaborative mode between universities and new art majors serving rural revitalization strategy, collaborative mode of agricultural management entities, collaborative mode of research institutes, collaborative mode of government departments, and so on. College and university collaborative innovation has its own set of rules. We can achieve the goal of collaborative innovation of art majors serving rural revitalization strategy agriculture industry-university-research by cooperating with other colleges and universities, new agricultural management entities, research institutes, and government departments. The new-type art major contributes to the rural revitalization strategy, and agricultural management entities collaborate. The new art major is based on the random forest algorithm, and agricultural management entities collaborate with agricultural industry-university-research innovation. It is necessary to fully integrate the superior resources of both universities and new agricultural management entities, as well as to create a trinity and coordinated development of scientific research achievements, technology, and products. In general, collaborative innovation research projects in industry-university-research, where art majors serve the strategic agriculture of rural revitalization, should be broken down into four stages: project establishment, project research, transformation of scientific research achievements, and industrialization of scientific research achievements.In order to select the best classification scheme to classify the strategy of art professional service for rural revitalization, the experiment is divided into six models, namely, SPR, sum, aut, oli, oli + txt, and oli + TXT + DEM. The classification results of different models are calculated into confusion matrix to obtain the overall classification accuracy, kappa coefficient, misclassification error, and missing error of each feature type. The differences between the classification results of each model are shown in Figures4–7 through error comparison.Figure 4
Error comparison.Figure 5
Error comparison.Figure 6
Error comparison.Figure 7
Error comparison.As shown in Figures4 and 5, the overall accuracy and kappa coefficient of six experimental models are compared: with the addition of different types of characteristic variables, the overall accuracy and kappa coefficient are gradually increasing. When spectral characteristic variables are added to a single-season spectral model (SPR, SUM, and AUT), the overall accuracy and kappa coefficient increase significantly, with the highest increases of 16 percent and 0.1710, respectively, from a single-season spectral model (SPR, SUM, and AUT) to a multiseason spectral model (OLI). The overall classification accuracy of OLI, OLI + TXT, and OLI + TXT + DEM models is 80.42%, 84.34%, and 85.56%, respectively, for adding texture feature variables and terrain feature variables, which is increased by 3.93% and 1.24%. From the standpoint of increasing range, texture variables have a greater impact on classification accuracy than terrain variables.The leakage error and misclassification error of various ground features under the three classification models (OLI, oli + txt, and oli + TXT + DEM) with high overall accuracy and kappa coefficient among the six models are compared, as shown in Figures6 and 7. The oli + TXT model with texture information and the oli + TXT + DEM model with terrain information reduce the misclassification phenomenon of other ground feature types when compared to the oli + T. To ensure the comparative experiment’s standardization, some data from four datasets are randomly selected as test samples, which are not included in the training samples, in order to compare the prediction accuracy of three algorithms: random forest, decision tree, and multiple linear regression, as shown in Figure 8.Figure 8
UCI dataset used in the experiment.The experimental results of the three algorithms are shown in Figures9 and 10, in which random forest, decision tree, and linear regression represent the accuracy.Figure 9
Experimental results.Figure 10
Experimental results.From the experimental results of Figures8–10, it can be seen that the prediction accuracy of the random forest algorithm is higher than that of the decision tree algorithm and linear regression algorithm. On the Wine dataset, the prediction accuracy of the decision tree and linear regression algorithms is nearly identical, but both are less accurate than the random forest algorithm. The other three datasets have inconsistent prediction accuracy, and both algorithms are less accurate than the random forest algorithm. The random forest algorithm outperforms the decision tree algorithm and linear regression in large and small datasets. The necessary conditions for art majors to serve the rural revitalization strategy are capital and market, based on the random forest algorithm, but lacking the transformation of scientific research achievements, whereas these two conditions are available for some agricultural management entities serving the rural revitalization strategy. Services for rural revitalization strategy professional services agricultural management entities require scientific research accomplishments from colleges and universities in order to improve the quality of their agricultural products or develop new agricultural products, resulting in a greater market share for agricultural products. In every stage of the research of collaborative innovation project of art specialty serving industry-university-research, the information service of rural revitalization strategy is essential. With it, the research for the rural revitalization strategy project can be completed successfully, and the goal of art specialty serving agriculture and industry-university-research collaboration can be realized. The collaborative innovation of the art major serving agriculture industry-university-research in the school refers to collaborative research of the art major serving agricultural research projects conducted by various internal scientific research units, as the name implies. Professional service collaboration innovation agriculture in industry-university-research necessitates collaboration between various disciplines and scientific research teams, research on a professional service agriculture project in art, and improving the school’s collaborative innovation ability. Agriculture in rural revitalization and strategic agriculture in industry-university-research are two areas where agriculture can help.


## 4.1. Strategy of Art Major Serving Rural Revitalization Based on Random Forest Algorithm

In the process of rural revitalization and construction, the demand for talents majoring in art and design is severe, and comprehensive personnel with solid design ability, professional knowledge, and certain traditional art foundation are needed. Of course, this demand also needs to be changed according to the special needs of regions, cultures, beliefs, and so on. In the strategic design of random forest art specialty serving rural revitalization, based on the completion of the overall and detailed design of the system, the specific coding realization of the strategy of art specialty serving rural revitalization based on the random forest algorithm is carried out. At present, the system has realized the functions of user registration, early warning analysis, and personal information management. There are five general requirements for implementing the strategy of art major serving rural revitalization, and under the general requirements, it is subdivided into three stages of objectives and tasks. Through comprehensive analysis and overall grasp of the general requirements, objectives, and tasks of the art major serving the rural revitalization strategy, it is found that it embodies the distinctive characteristics of nationality, inclusiveness, and long-term. The existing literature related research at home and abroad mainly focuses on the information service strategy, mode, system, platform, and information transfer of the art major serving the rural revitalization strategy, but there is little research on the information service model of collaborative innovation in industry-university-research. Under the background of art major serving rural revitalization strategy, the knowledge transfer of agricultural industry-university-research collaborative innovation refers to the transfer of knowledge capital to new agricultural management entities. Therefore, from the perspective of knowledge transfer, according to the diversified information service mode of agricultural industry-university-research collaborative innovation, this study makes an in-depth discussion and research on the information service model of art major serving rural revitalization strategy. After completing the relevant functions of the system, test and verify the functions of art professional service rural revitalization strategy and further improve the functions of art professional service rural revitalization strategy according to the test results. Many colleges and universities believe that rural construction is not easy to achieve results. Both capital investment and talent investment are not in direct proportion to the results. The results are slow, which is a waste of resources. If these resources are invested in other teaching and research, the benefits will be greater. The fusion methods of the random forest algorithm are diverse, and how to choose is based on the actual situation of the countryside. As a long-term development project, rural revitalization and construction must be guaranteed in terms of funds, not only in the process of construction but also in the training of art design major in new rural construction in colleges and universities. Many of them do not have direct funds, and the required expenses come from teaching funds, which is a very realistic and obvious problem for colleges and universities. Colleges and universities have great pressure on this project. If this problem is not solved, the investment of teachers will always be a serious problem. In the rural revitalization construction, we should pay attention to the protection of local intangible cultural heritage and historical and cultural buildings, which will require a large number of art talents. Therefore, the transportation of such talents is the responsibility of higher vocational colleges, and the introduction of such talents is the responsibility of the government.Based on the random forest algorithm, talents who meet the requirements of art majors to serve the rural culture construction can serve the rural revitalization construction as a disseminator of the rural revitalization strategic culture, allowing them to put all of their knowledge to good use and promote the rural culture construction. Rural culture must be revitalized in order to fully arouse people’s enthusiasm, and students majoring in art and design should use their professional expertise to create a new rural culture with distinct characteristics. Industry prosperity is the economic foundation for implementing the art major serving rural revitalization strategy. In contrast to the requirement of “production development” in the construction of new socialist countryside, production development refers to the improvement and optimization of agricultural production structure, development mode, and organic composition. The development of the existing strategy of art major serving rural revitalization is based on the basic conditions of agricultural production. It focuses more on improving the degree of integration between agricultural production and secondary and tertiary industries, so that farmers, as market participants, can share the value-added benefits of industrial development in the integration of tertiary industries and strive for “people and production prosperity.” Finally, the coordinated development of the agricultural system and nonagricultural industry system will realize the revitalization of art major serving rural industries. Students are urged to actively participate in the rural revitalization construction, go into the countryside, take root in the countryside, make their own contributions to the rural revitalization strategy, and reflect their sense of social responsibility as contemporary college students. The strategy of art specialty serving the rural revitalization should take the rural cultural construction as one of the courses, carry out special learning, carry out in-depth learning in combination with the rural revitalization strategy, and import professional talents into the rural revitalization construction. On this basis, I believe that with the active cooperation of colleges and universities, the construction and development of villages and villages will be very rapid. The results will be more obvious and easier to achieve. The school has limited teachers, and rural construction will consume teachers. However, this investment should be made. Rural construction is a necessary process for social development. At present, China’s rural cultural revitalization and construction institutions are mostly county level or village level cultural base stations. The characteristics of regional cultural stations are often characterized by low quality and few personnel, which seriously limit the distance of rural cultural construction. Therefore, it is very important for colleges and universities to serve rural cultural construction. It is necessary to build and continuously optimize the modern agricultural system and provide external conditions for the development of China’s rural revitalization strategic agricultural modernization through measures such as improving the scientific and technological content of art specialty service agriculture and improving the art specialty service agricultural protection and service system.


## 4.2. Experimental Results and Analysis

Collaborative modes of agricultural management entities, collaborative modes of research institutes, collaborative modes of government departments, and other collaborative innovation modes of professional service agriculture in industry-university-research include the collaborative mode, collaborative mode between universities and new art majors serving rural revitalization strategy, collaborative mode of agricultural management entities, collaborative mode of research institutes, collaborative mode of government departments, and so on. College and university collaborative innovation has its own set of rules. We can achieve the goal of collaborative innovation of art majors serving rural revitalization strategy agriculture industry-university-research by cooperating with other colleges and universities, new agricultural management entities, research institutes, and government departments. The new-type art major contributes to the rural revitalization strategy, and agricultural management entities collaborate. The new art major is based on the random forest algorithm, and agricultural management entities collaborate with agricultural industry-university-research innovation. It is necessary to fully integrate the superior resources of both universities and new agricultural management entities, as well as to create a trinity and coordinated development of scientific research achievements, technology, and products. In general, collaborative innovation research projects in industry-university-research, where art majors serve the strategic agriculture of rural revitalization, should be broken down into four stages: project establishment, project research, transformation of scientific research achievements, and industrialization of scientific research achievements.In order to select the best classification scheme to classify the strategy of art professional service for rural revitalization, the experiment is divided into six models, namely, SPR, sum, aut, oli, oli + txt, and oli + TXT + DEM. The classification results of different models are calculated into confusion matrix to obtain the overall classification accuracy, kappa coefficient, misclassification error, and missing error of each feature type. The differences between the classification results of each model are shown in Figures4–7 through error comparison.Figure 4
Error comparison.Figure 5
Error comparison.Figure 6
Error comparison.Figure 7
Error comparison.As shown in Figures4 and 5, the overall accuracy and kappa coefficient of six experimental models are compared: with the addition of different types of characteristic variables, the overall accuracy and kappa coefficient are gradually increasing. When spectral characteristic variables are added to a single-season spectral model (SPR, SUM, and AUT), the overall accuracy and kappa coefficient increase significantly, with the highest increases of 16 percent and 0.1710, respectively, from a single-season spectral model (SPR, SUM, and AUT) to a multiseason spectral model (OLI). The overall classification accuracy of OLI, OLI + TXT, and OLI + TXT + DEM models is 80.42%, 84.34%, and 85.56%, respectively, for adding texture feature variables and terrain feature variables, which is increased by 3.93% and 1.24%. From the standpoint of increasing range, texture variables have a greater impact on classification accuracy than terrain variables.The leakage error and misclassification error of various ground features under the three classification models (OLI, oli + txt, and oli + TXT + DEM) with high overall accuracy and kappa coefficient among the six models are compared, as shown in Figures6 and 7. The oli + TXT model with texture information and the oli + TXT + DEM model with terrain information reduce the misclassification phenomenon of other ground feature types when compared to the oli + T. To ensure the comparative experiment’s standardization, some data from four datasets are randomly selected as test samples, which are not included in the training samples, in order to compare the prediction accuracy of three algorithms: random forest, decision tree, and multiple linear regression, as shown in Figure 8.Figure 8
UCI dataset used in the experiment.The experimental results of the three algorithms are shown in Figures9 and 10, in which random forest, decision tree, and linear regression represent the accuracy.Figure 9
Experimental results.Figure 10
Experimental results.From the experimental results of Figures8–10, it can be seen that the prediction accuracy of the random forest algorithm is higher than that of the decision tree algorithm and linear regression algorithm. On the Wine dataset, the prediction accuracy of the decision tree and linear regression algorithms is nearly identical, but both are less accurate than the random forest algorithm. The other three datasets have inconsistent prediction accuracy, and both algorithms are less accurate than the random forest algorithm. The random forest algorithm outperforms the decision tree algorithm and linear regression in large and small datasets. The necessary conditions for art majors to serve the rural revitalization strategy are capital and market, based on the random forest algorithm, but lacking the transformation of scientific research achievements, whereas these two conditions are available for some agricultural management entities serving the rural revitalization strategy. Services for rural revitalization strategy professional services agricultural management entities require scientific research accomplishments from colleges and universities in order to improve the quality of their agricultural products or develop new agricultural products, resulting in a greater market share for agricultural products. In every stage of the research of collaborative innovation project of art specialty serving industry-university-research, the information service of rural revitalization strategy is essential. With it, the research for the rural revitalization strategy project can be completed successfully, and the goal of art specialty serving agriculture and industry-university-research collaboration can be realized. The collaborative innovation of the art major serving agriculture industry-university-research in the school refers to collaborative research of the art major serving agricultural research projects conducted by various internal scientific research units, as the name implies. Professional service collaboration innovation agriculture in industry-university-research necessitates collaboration between various disciplines and scientific research teams, research on a professional service agriculture project in art, and improving the school’s collaborative innovation ability. Agriculture in rural revitalization and strategic agriculture in industry-university-research are two areas where agriculture can help.


## 5. Conclusions

The exploration and use of traditional culture are inextricably linked to the relevant construction of rural revitalization. The preservation and inheritance of rural culture must be done during the construction process in order to build a distinctive and sustainable rural culture. We can effectively summarize and cultivate the key factors with high comprehensive quality by introducing the random forest method to obtain the hidden value in the data and making full use of the data of learning and life performance of art specialty serving the rural revitalization strategy. The strategy of innovating and expanding art professional services for rural revitalization has a key impact on development, according to the rules generated by the random forest algorithm. The art design specialty of colleges and universities should make full use of the traditional culture to be excavated in the process of serving rural revitalization and construction. To protect this kind of culture, the local government and the people must work together. Simultaneously, it should introduce some advanced culture, carry on tradition, and develop and grow in cultural integration. The significance of random forest characteristic variables can be used as the foundation for feature selection in order to effectively reduce data dimension. The combination of reduced dimensional comprehensive information and the random forest algorithm achieves a good balance between classification efficiency, accuracy, and applicability, resulting in a reliable method and favorable basis for extracting land use information in agricultural farming areas in the future.



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