# Analysis of Rural Tourism and Environmental Influencing Factors Based on Analytic Hierarchy Model

Authors: Na Guo
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

This research focuses on the analytic hierarchy model in the decision-making system that has a more complex structure and maintains the stability of the system, models the application process with the complexity and diversity of the rural economy, collects sample data with the help of different types of rural tourism questionnaire surveys, and integrates the data of rural tourism and other tourism into the model. The following are obtained: (1) During the level analysis, each phenotype track uses RRM,C1=0.26, C2=0.223, C3=0.52, C4=0.25, C5=0.833, C6=0.442, C7=0.75, C8=0.127, C9=0.876, C10=0.792, C11=0.049, C12=0.16, C13=0.166, and C14=0.049. The problems of the complex structure of the evaluation can be divided into simple analysis modules, and each module is analyzed at a level. The phenotypic trajectory of each individual is divided into target layer, standard layer, and scheme layer. (2) Arrangement and decision modeling were performed according to one or several indicators of different factors. In the hierarchical random regression model, APC=0.214, UPUA=0.042, TO=0.081, YPUA=0.082, PCP=0.068, and APS=0.067. The characteristic quantity analysis of different environments can be carried out, and the amplitude error and frequency error obtained are relatively small. IAND=0.115, AVA=0.198, RD=0.119, PI=0.041, PCCL=0.142, IOC=0.201, and DSTC=0.069. The comparison shows that the hierarchical analysis model is better than the hierarchical random regression model. (3) High-efficiency hybrid model correlation acceleration is the worst model. The experimental data are APC=0.147, UPUA=0.029, TO=0.055, YPUA=0.06, PCP=0.047, APS=0.046, IAND=0.079, AVA=0.136, RD=0.082, PI=0.028, PCCL=0.098, IOC=0.139, and DSTC=0.048. (4) The predicted 2020 data and the actual data have small errors. The data obtained by the AHP model is GDP=1262.1, finance=185.09, budget=68, tax=51.92, fundbudget=69.23, transferincome=40.14, debtincome=7.73, disposablefinancialpower=177.37, fiscalexpenditure=191.26, publicbudget=88.68, governmentexpenditure=71.39, transferexpenditure=23.46, debtexpenditure=7.73, and lastyearbalance=2.39.


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## Body

## 1. Introduction

The analytic hierarchy model is proposed on the basis of operation research. It is suitable for decision-making systems with complex structures and maintaining system stability. In the process of tracing the current of the research problem, the decision criterion distance between the points of the analytic hierarchy process is used as the reference quantity of the experimental model. It can be used to maintain system stability and solve decision-making problems that have more decision-making criteria and are not so easy to quantify. When the results of longitudinal data expansion prediction conflict with the decision-maker’s brain judgment consciousness, reasonable quantitative reasoning can be carried out. Make decision analysis on the possible consequences of behavior [1–3]. Modeling is based on the individual’s phenotypic trajectory. When there are many decision-making criteria in the hierarchical random regression model, factor characteristics are used for association labeling. In order to avoid the confusion of the algorithm, the decision-maker must first logically sort out to avoid running errors. The analytic hierarchy model has been developed rapidly in recent years in the vertical data expansion. The analytic hierarchy model can track the power flow, using the multiple linear regression trajectory distance between the nodes as the reference quantity, clustering the nodes with close distances from the high-efficiency hybrid model. The statistical utility system is divided into several subareas, and the phenotypic stratification is studied, and a reasonable association analysis result is obtained. The basic step of the analytic hierarchy process is to expand the original data into a full range of longitudinal data analysis. The construction of the hierarchical structure model reduces the dimension of repeated measures [4–6]. Reducing the dimensionality of repeated measurement can divide the problem of the complex structure of the evaluation into simple analysis modules and perform a hierarchical analysis on each module. The affiliation of RRM used by Hi-RRM is top-down, and the phenotypic trajectory of each individual is divided into a target layer, a standard layer, and a scheme layer. The target layer is to capture and dynamically process the phenotypic trajectory to achieve the extraction of the required features. The middle layer is the filtering of data, which is arranged, and decision-making modeling according to one or several indicators of different factors. When the middle layer contains multiple indicators at the same time, RRM can realize the optimization of the subcriteria layer between different layers. The scheme layer can refine the criterion layer [7]. Compare the factors in the matrix construction. For different matrices in the plan layer, the criterion layer is judged to construct different plan levels. Whether the judgment matrix needs to rebuild the matrix plan, the decision of the criterion-level judgment plan is made according to the matrix judgment. The hierarchical random regression model scaling method and the multivariate mixed model (mvLMM) are used to construct the correlation of the judgment matrix. The specific method is to decompose the spectrum of the regression covariance matrix and write it as a numerical value through the relationship of the pairwise comparison results. mvLMM is transformed into multiple linear regression [8, 9]. Use the phenotype of the power angle rocking curve similarity analysis to continue with the regression markers [10–12]. Through time series data mining relationships and dynamic time-warping distances, software time domain faults are simulated in an efficient hybrid model. In the field of time series data mining, the calculation method of bending distance is realized, while the calculation efficiency is improved. Compared with the existing RRM-based bending distance analysis, the regression marker can perform multiple linear regressions on the rocking curve data meter of each motor calculated by the software time domain fault simulation. And in the experiment, the statistical utility of the regression labeling model is proved. When calculating the time domain similarity index, the longitudinal data analysis index is used to measure the dimensionality. Performing dimensionality reduction test processing on the Hi-RRM modeling data first can significantly reduce the complexity of repeated measurements. By simplifying the phenotypic trajectory and using the nodal admittance matrix of the RRM system, a multivariate hybrid similarity matrix reflecting the distance of the phenotypic trajectory is obtained. Using spectral decomposition and regression covariance matrix can realize adaptive parameter processing for fuzzy clustering algorithm. In the analysis layer, the importance of the analysis unit parameter factors is sorted [13–15]. Using the analytic hierarchy process evaluation model, rural tourism can be established as a new model with traditional, simple, and natural characteristics. On the basis of the original pleasant and beautiful natural landscape, the model is updated and transformed. It forms a pure rural tourism model in which tourists move in the countryside after they come. The vision of the countryside is broad. Everywhere, you can get in close contact with nature and conduct questionnaire surveys on tourists’ questions. After the data of the questionnaire is sorted, the algorithm of the rural tourism model database is substituted into the evaluation model for consistency test. By investigating the weights of the various factors in the activities of the rural local cultural traditions, the main influencing factors of the slow changes in the social structure and culture are found. This study uses the analytic hierarchy process, focusing on the decision-making system that the analytic hierarchy model has a more complex structure and maintains the stability of the system, models the application process with the complexity and diversity of the rural economy, collects sample data with the help of different types of rural tourism questionnaire surveys, and integrates rural tourism and other tourism data into the model.


## 2. Hierarchical Analysis Modeling Based on Rural Tourism

### 2.1. Scenario Analysis Process Method

Rural tourism and environmental impact are based on the local natural ecology. Carry out analytic hierarchy process modeling application to the collected pastoral scenery and folk customs. Based on farmers’ production and living resources, it will provide tourists with rural leisure tourism services by means of surveys. Collect sample data to predict and optimize the local industrial structure. AHP modeling needs to add connotative value to unique cultural resources. The risk factors in the project should be reasonably evaluated and forecasted. Check carefully as the detection system will substitute the sample data into the model. After symptom deletion, selection, and identification, a perfect closed loop is formed. After calculating the weights of the rural unique natural resource stratification indicators, the consistency test is done. After the test is passed, the index weights of the model’s validity are summarized, and the main factors that affect tourism and the environment are analyzed. As shown in Figure1 [16–18].Figure 1
Flow chart of scenario analysis.


### 2.2. Indicator System

The index system for evaluating rural tourism includes many aspects. Substitute the collated political, economic, and environmental sample data into the model to calculate the corresponding stratified index weights. After unifying the management technology and social risks, a consistency test is made, as shown in Figure2.Figure 2
Index system.


### 2.3. Advantages of Rural Tourism

Compared with other tourism methods, this new type of tourism has a broader field of vision. There are some local cultural and traditional activities. Both the limitation of the scope of activities and the size of the scale are dominant. The frequency of the social structure determines the vibration law of the cultural change curve. Rich in the frequency domain characteristics of traditional colors, when the vibration complexity and the diversity cycle are synchronized, the consistency of the geographical environment should be considered as much as possible. When determining the level of the matrix that is controlled by the locality, the time domain characteristic quantity of the historical and cultural curve is considered to be the most important. The frequency, amplitude, and damping factor of the frequency domain feature quantities are the frequency, amplitude, and damping factor from high to low.


## 2.1. Scenario Analysis Process Method

Rural tourism and environmental impact are based on the local natural ecology. Carry out analytic hierarchy process modeling application to the collected pastoral scenery and folk customs. Based on farmers’ production and living resources, it will provide tourists with rural leisure tourism services by means of surveys. Collect sample data to predict and optimize the local industrial structure. AHP modeling needs to add connotative value to unique cultural resources. The risk factors in the project should be reasonably evaluated and forecasted. Check carefully as the detection system will substitute the sample data into the model. After symptom deletion, selection, and identification, a perfect closed loop is formed. After calculating the weights of the rural unique natural resource stratification indicators, the consistency test is done. After the test is passed, the index weights of the model’s validity are summarized, and the main factors that affect tourism and the environment are analyzed. As shown in Figure1 [16–18].Figure 1
Flow chart of scenario analysis.


## 2.2. Indicator System

The index system for evaluating rural tourism includes many aspects. Substitute the collated political, economic, and environmental sample data into the model to calculate the corresponding stratified index weights. After unifying the management technology and social risks, a consistency test is made, as shown in Figure2.Figure 2
Index system.


## 2.3. Advantages of Rural Tourism

Compared with other tourism methods, this new type of tourism has a broader field of vision. There are some local cultural and traditional activities. Both the limitation of the scope of activities and the size of the scale are dominant. The frequency of the social structure determines the vibration law of the cultural change curve. Rich in the frequency domain characteristics of traditional colors, when the vibration complexity and the diversity cycle are synchronized, the consistency of the geographical environment should be considered as much as possible. When determining the level of the matrix that is controlled by the locality, the time domain characteristic quantity of the historical and cultural curve is considered to be the most important. The frequency, amplitude, and damping factor of the frequency domain feature quantities are the frequency, amplitude, and damping factor from high to low.


## 3. The Application of Analytic Hierarchy Process in Rural Tourism

### 3.1. Hierarchical Random Regression Model (Hi-RRM) [19–21]

(1)aij=xij−mjMj−mj.


#### 3.1.1. Political and Economic Risks

(2)aij=xij−minxjmaxxj−minxj.


#### 3.1.2. Complexity of Tourists

(3)aij=Mj−sijMj−mj,aij=maxxj−xijmaxxj−minxj,Mj=maxixij.


#### 3.1.3. Diversity Modeling

(4)mj=minixij.


### 3.2. Analytical Hierarchy Process (AHP) [22–24]

(5)R−rstmn,s=1,2,⋯,m;t=1,2,⋯,n.


#### 3.2.1. Data Collation

(6)Ht=−∑s=1mfstInfstInm,s=1,2,⋯,n.


#### 3.2.2. Local Natural Ecology

(7)fst=bst∑s=1mbst,fst=1+b∑s=1m1+bst.


#### 3.2.3. Environmental Management Risk

(8)W=wt1×n,wt=1−Htn−∑t=1nHt,CI=λmax−nn−1.


#### 3.2.4. Local Industrial Structure

(9)wi=∑j=1nbijaj,i=1,2,⋯,m,f1dijk=dijk−2−1,f2dijk=dijk−4−2,f3dijk=dijk−6−3.


### 3.3. High-Efficiency Hybrid Model Correlation Acceleration (EMMAX) [25, 26]

(10)Xijh=∑k=1pfhdijk,Xij=∑h=15Xijk.


#### 3.3.1. Cultural Resources Increase

(11)rijh=XijhXij,uij=i=1,2,⋯,ni.


#### 3.3.2. Technology Risk

(12)Ai=ai1,ai2,⋯,aini,Bii=1,2,⋯,m.


#### 3.3.3. Risk Factors

(13)Bi=Ai•Ri=b1,b2,⋯,b5,C=A×B=ai1,ai2,⋯,aini×b1,b2,⋯,b5,E=1,2,3,4,5,Z=C·ET.


## 3.1. Hierarchical Random Regression Model (Hi-RRM) [19–21]

(1)aij=xij−mjMj−mj.


### 3.1.1. Political and Economic Risks

(2)aij=xij−minxjmaxxj−minxj.


### 3.1.2. Complexity of Tourists

(3)aij=Mj−sijMj−mj,aij=maxxj−xijmaxxj−minxj,Mj=maxixij.


### 3.1.3. Diversity Modeling

(4)mj=minixij.


## 3.1.1. Political and Economic Risks

(2)aij=xij−minxjmaxxj−minxj.


## 3.1.2. Complexity of Tourists

(3)aij=Mj−sijMj−mj,aij=maxxj−xijmaxxj−minxj,Mj=maxixij.


## 3.1.3. Diversity Modeling

(4)mj=minixij.


## 3.2. Analytical Hierarchy Process (AHP) [22–24]

(5)R−rstmn,s=1,2,⋯,m;t=1,2,⋯,n.


### 3.2.1. Data Collation

(6)Ht=−∑s=1mfstInfstInm,s=1,2,⋯,n.


### 3.2.2. Local Natural Ecology

(7)fst=bst∑s=1mbst,fst=1+b∑s=1m1+bst.


### 3.2.3. Environmental Management Risk

(8)W=wt1×n,wt=1−Htn−∑t=1nHt,CI=λmax−nn−1.


### 3.2.4. Local Industrial Structure

(9)wi=∑j=1nbijaj,i=1,2,⋯,m,f1dijk=dijk−2−1,f2dijk=dijk−4−2,f3dijk=dijk−6−3.


## 3.2.1. Data Collation

(6)Ht=−∑s=1mfstInfstInm,s=1,2,⋯,n.


## 3.2.2. Local Natural Ecology

(7)fst=bst∑s=1mbst,fst=1+b∑s=1m1+bst.


## 3.2.3. Environmental Management Risk

(8)W=wt1×n,wt=1−Htn−∑t=1nHt,CI=λmax−nn−1.


## 3.2.4. Local Industrial Structure

(9)wi=∑j=1nbijaj,i=1,2,⋯,m,f1dijk=dijk−2−1,f2dijk=dijk−4−2,f3dijk=dijk−6−3.


## 3.3. High-Efficiency Hybrid Model Correlation Acceleration (EMMAX) [25, 26]

(10)Xijh=∑k=1pfhdijk,Xij=∑h=15Xijk.


### 3.3.1. Cultural Resources Increase

(11)rijh=XijhXij,uij=i=1,2,⋯,ni.


### 3.3.2. Technology Risk

(12)Ai=ai1,ai2,⋯,aini,Bii=1,2,⋯,m.


### 3.3.3. Risk Factors

(13)Bi=Ai•Ri=b1,b2,⋯,b5,C=A×B=ai1,ai2,⋯,aini×b1,b2,⋯,b5,E=1,2,3,4,5,Z=C·ET.


## 3.3.1. Cultural Resources Increase

(11)rijh=XijhXij,uij=i=1,2,⋯,ni.


## 3.3.2. Technology Risk

(12)Ai=ai1,ai2,⋯,aini,Bii=1,2,⋯,m.


## 3.3.3. Risk Factors

(13)Bi=Ai•Ri=b1,b2,⋯,b5,C=A×B=ai1,ai2,⋯,aini×b1,b2,⋯,b5,E=1,2,3,4,5,Z=C·ET.


## 4. Simulation Experiment

### 4.1. Development Trend

Calculate the existing data of rural tourism, including the number of travel orders and the number of new tourists. Advertise online reading page views, search times for keywords, and search index. The number of visits, the number of people who have experienced, etc. are marked and correlated. Get current tourism trends, as shown in Table1 and Figure 3.Table 1
Development trend.
Hierarchical analysisCountryside tourOdd numberAddReadKeywordsNumber of trialsSearch indexExperience number1980883826044443542316509269154522323116195974581542029121010714106871051842255108324312685282135015733647722001135425253418160639423018601396812524542740161079133753838294Figure 3
Development trend.


### 4.2. Weight Indicators

Correspond to different weight indicators for different features. As shown in Table2 and Figure 4, when performing hierarchical analysis, each phenotype track uses RRM, C1=0.26, C2=0.223, C3=0.52, C4=0.25, C5=0.833, C6=0.442, C7=0.75, C8=0.127, C9=0.876, C10=0.792, C11=0.049, C12=0.16, C13=0.166, and C14=0.049. Use secondary weight data for simulation experiments to improve calculation efficiency in high-efficiency mixing.Table 2
Weight indicators.
First level indicatorWeightsSecondary indicatorsWeightsHierarchical total orderScheme rankingB10.495C10.260.0481C20.2230.0414C30.520.0462C40.250.2587B20.246C50.8330.4123C60.4420.2198C70.750.3715B30.186C80.1270.0316C90.8760.0219C100.7920.01910B40.072C110.0490.00314C120.160.00112C130.1660.01211C140.0490.0031Figure 4
Weight indicators.


### 4.3. Model Comparison

Import the model according to the collected indicators of the existing time domain data set. In the analytic hierarchy model,APC=0.786, UPUA=0.919, TO=0.919, YPUA=0.913, PCP=0.931, APS=0.932, and IAND=0.884. Using decomposition simulation credibility evaluation method to analyze other indicators in the model, we get AVA=0.802, RD=0.881, PI=0.958, PCCL=0.857, IOC=0.798, and DSTC=0.93. In the hierarchical random regression model (Hi-RRM), APC=0.214, UPUA=0.042, TO=0.081, YPUA=0.082, PCP=0.068, APS=0.067, IAND=0.115, AVA=0.198, RD=0.119, PI=0.041, PCCL=0.142, IOC=0.201, and DSTC=0.069. The comparison shows that the hierarchical analysis model is better than the hierarchical random regression model. By analyzing the characteristic quantities of different environments, the amplitude error and frequency error obtained are relatively small. The high-efficiency hybrid model correlation acceleration is the worst model. The experimental data are APC=0.147, UPUA=0.029, TO=0.055, YPUA=0.06, PCP=0.047, APS=0.046, IAND=0.079, AVA=0.136, RD=0.082, PI=0.028, PCCL=0.098, IOC=0.139, and DSTC=0.048, as shown in Table 3 and Figure 5.Table 3
Model comparison.
SubsystemSingle indexAHPHi-RRMEMMAXProduction resourcesAPC0.7860.2140.147UPUA0.9190.0420.029AvailabilityTO0.9190.0810.055YPUA0.9130.0820.06PCP0.9310.0680.047APS0.9320.0670.046IAND0.8840.1150.079AVA0.8020.1980.136Acquisition capacityRD0.8810.1190.082PI0.9580.0410.028PCCL0.8570.1420.098Utilization levelIOC0.7980.2010.139DSTC0.930.0690.048Figure 5
Model comparison.


### 4.4. Rural Tourism Management Strategy

Planning and forecasting business strategies include screening strategies for project sponsorship conditions. When conducting social capital financing, filter and screen. The selection of the operation team should also be cautious, and the method of curve error evaluation should be used to help the operation. Use curve similarity analysis to judge marketing strategies. Select the time series to evaluate the subcontrol strategy indicators, and analyze the results according to the curve amplitude. Have an in-depth understanding of internal and external policies in a timely manner to ensure normal operation and maximization of benefits, as shown in Table4 and Table 5.Table 4
Business strategy.
Business strategyProject condition screening strategySocial capital side selection strategyTeam strategyMarketing strategyProject condition screening10.43623.78016.2714Social capital side selection strategy2.292515.57238.3965Professional service team0.26450.179512.7108Marketing pricing strategy0.15950.11910.36891Table 5
Risk control strategy.
Risk control strategyInternal and external policiesProject operationLocal governmentCEDITInternal and external policies10.54445.43252.0972Project operation1.836817.21093.7674Local government0.18410.138710.3008CEDIT0.47680.26543.32491


### 4.5. Simulation Experiment and Prediction

In terms of GDP, finances, budgets, taxation, fund budgets, transfer income, debt income, disposable financial resources, etc., the predicted 2020 data and the real data have relatively small errors. The data obtained by the AHP model isGDP=1262.1, finance=185.09, budget=68, tax=51.92, fundbudget=69.23, transferincome=40.14, debtincome=7.73, disposablefinancialpower=177.37, fiscalexpenditure=191.26, publicbudget=88.68, governmentexpenditure=71.39, transferexpenditure=23.46, debtexpenditure=7.73, and lastyearbalance=2.39. As shown in Tables 6 and 7 and Figures 6 and 7.Table 6
Simulation experiment.
Main indicators201820192020Growth rateGDP1150.371196.31262.14.74%Financial141.18154.89185.0914.50%Budget70.3670.668-1.69%Tax50.0552.6851.921.85%Fund budget41.0342.2969.2329.90%Transfer income29.2937.9140.1417.07%Debt income0.54.097.73293.19%Disposable financial resources140.68150.8177.3712.29%Fiscal expenditure144.5158.3191.2615.05%Public budget84.0791.8588.682.71%Government expenditure41.7442.771.3930.78%Transfer expenditure18.3819.6623.4612.98%Debt expenditure0.324.097.73391.49%Last year balance1.872.362.3913.05%Table 7
Simulation forecast.
YearsBRTCountryside tourPPPEnvironmental factorsPercentage202029.261920910.22%202126.691945930.48%202221.956272960.75%202319.36166980.67%202419.875918101.86%202560.08581881.78%202637.682345691.71%202732.536188131.65%202825.825666411.59%202934.361998111.55%203023.291917221.51%203116.857117551.46%203216.876817891.42%203315.623617241.38%203418.681324131.86%203517.175617131.30%Figure 6
Simulation experiment.Figure 7
Simulation forecast.In the future data measurement using the analytic hierarchy model, it is concluded that it will reach a peak in 2026. In 2026,BRT=37.68, countrysidetour=23, PPP=45, environmentalfactors=69, and percentage=0.0171. In 2020, it is close to the real data, BRT=29.26, countrysidetour=19, PPP=20, environmentalfactors=91, and percentage=0.0022.


## 4.1. Development Trend

Calculate the existing data of rural tourism, including the number of travel orders and the number of new tourists. Advertise online reading page views, search times for keywords, and search index. The number of visits, the number of people who have experienced, etc. are marked and correlated. Get current tourism trends, as shown in Table1 and Figure 3.Table 1
Development trend.
Hierarchical analysisCountryside tourOdd numberAddReadKeywordsNumber of trialsSearch indexExperience number1980883826044443542316509269154522323116195974581542029121010714106871051842255108324312685282135015733647722001135425253418160639423018601396812524542740161079133753838294Figure 3
Development trend.


## 4.2. Weight Indicators

Correspond to different weight indicators for different features. As shown in Table2 and Figure 4, when performing hierarchical analysis, each phenotype track uses RRM, C1=0.26, C2=0.223, C3=0.52, C4=0.25, C5=0.833, C6=0.442, C7=0.75, C8=0.127, C9=0.876, C10=0.792, C11=0.049, C12=0.16, C13=0.166, and C14=0.049. Use secondary weight data for simulation experiments to improve calculation efficiency in high-efficiency mixing.Table 2
Weight indicators.
First level indicatorWeightsSecondary indicatorsWeightsHierarchical total orderScheme rankingB10.495C10.260.0481C20.2230.0414C30.520.0462C40.250.2587B20.246C50.8330.4123C60.4420.2198C70.750.3715B30.186C80.1270.0316C90.8760.0219C100.7920.01910B40.072C110.0490.00314C120.160.00112C130.1660.01211C140.0490.0031Figure 4
Weight indicators.


## 4.3. Model Comparison

Import the model according to the collected indicators of the existing time domain data set. In the analytic hierarchy model,APC=0.786, UPUA=0.919, TO=0.919, YPUA=0.913, PCP=0.931, APS=0.932, and IAND=0.884. Using decomposition simulation credibility evaluation method to analyze other indicators in the model, we get AVA=0.802, RD=0.881, PI=0.958, PCCL=0.857, IOC=0.798, and DSTC=0.93. In the hierarchical random regression model (Hi-RRM), APC=0.214, UPUA=0.042, TO=0.081, YPUA=0.082, PCP=0.068, APS=0.067, IAND=0.115, AVA=0.198, RD=0.119, PI=0.041, PCCL=0.142, IOC=0.201, and DSTC=0.069. The comparison shows that the hierarchical analysis model is better than the hierarchical random regression model. By analyzing the characteristic quantities of different environments, the amplitude error and frequency error obtained are relatively small. The high-efficiency hybrid model correlation acceleration is the worst model. The experimental data are APC=0.147, UPUA=0.029, TO=0.055, YPUA=0.06, PCP=0.047, APS=0.046, IAND=0.079, AVA=0.136, RD=0.082, PI=0.028, PCCL=0.098, IOC=0.139, and DSTC=0.048, as shown in Table 3 and Figure 5.Table 3
Model comparison.
SubsystemSingle indexAHPHi-RRMEMMAXProduction resourcesAPC0.7860.2140.147UPUA0.9190.0420.029AvailabilityTO0.9190.0810.055YPUA0.9130.0820.06PCP0.9310.0680.047APS0.9320.0670.046IAND0.8840.1150.079AVA0.8020.1980.136Acquisition capacityRD0.8810.1190.082PI0.9580.0410.028PCCL0.8570.1420.098Utilization levelIOC0.7980.2010.139DSTC0.930.0690.048Figure 5
Model comparison.


## 4.4. Rural Tourism Management Strategy

Planning and forecasting business strategies include screening strategies for project sponsorship conditions. When conducting social capital financing, filter and screen. The selection of the operation team should also be cautious, and the method of curve error evaluation should be used to help the operation. Use curve similarity analysis to judge marketing strategies. Select the time series to evaluate the subcontrol strategy indicators, and analyze the results according to the curve amplitude. Have an in-depth understanding of internal and external policies in a timely manner to ensure normal operation and maximization of benefits, as shown in Table4 and Table 5.Table 4
Business strategy.
Business strategyProject condition screening strategySocial capital side selection strategyTeam strategyMarketing strategyProject condition screening10.43623.78016.2714Social capital side selection strategy2.292515.57238.3965Professional service team0.26450.179512.7108Marketing pricing strategy0.15950.11910.36891Table 5
Risk control strategy.
Risk control strategyInternal and external policiesProject operationLocal governmentCEDITInternal and external policies10.54445.43252.0972Project operation1.836817.21093.7674Local government0.18410.138710.3008CEDIT0.47680.26543.32491


## 4.5. Simulation Experiment and Prediction

In terms of GDP, finances, budgets, taxation, fund budgets, transfer income, debt income, disposable financial resources, etc., the predicted 2020 data and the real data have relatively small errors. The data obtained by the AHP model isGDP=1262.1, finance=185.09, budget=68, tax=51.92, fundbudget=69.23, transferincome=40.14, debtincome=7.73, disposablefinancialpower=177.37, fiscalexpenditure=191.26, publicbudget=88.68, governmentexpenditure=71.39, transferexpenditure=23.46, debtexpenditure=7.73, and lastyearbalance=2.39. As shown in Tables 6 and 7 and Figures 6 and 7.Table 6
Simulation experiment.
Main indicators201820192020Growth rateGDP1150.371196.31262.14.74%Financial141.18154.89185.0914.50%Budget70.3670.668-1.69%Tax50.0552.6851.921.85%Fund budget41.0342.2969.2329.90%Transfer income29.2937.9140.1417.07%Debt income0.54.097.73293.19%Disposable financial resources140.68150.8177.3712.29%Fiscal expenditure144.5158.3191.2615.05%Public budget84.0791.8588.682.71%Government expenditure41.7442.771.3930.78%Transfer expenditure18.3819.6623.4612.98%Debt expenditure0.324.097.73391.49%Last year balance1.872.362.3913.05%Table 7
Simulation forecast.
YearsBRTCountryside tourPPPEnvironmental factorsPercentage202029.261920910.22%202126.691945930.48%202221.956272960.75%202319.36166980.67%202419.875918101.86%202560.08581881.78%202637.682345691.71%202732.536188131.65%202825.825666411.59%202934.361998111.55%203023.291917221.51%203116.857117551.46%203216.876817891.42%203315.623617241.38%203418.681324131.86%203517.175617131.30%Figure 6
Simulation experiment.Figure 7
Simulation forecast.In the future data measurement using the analytic hierarchy model, it is concluded that it will reach a peak in 2026. In 2026,BRT=37.68, countrysidetour=23, PPP=45, environmentalfactors=69, and percentage=0.0171. In 2020, it is close to the real data, BRT=29.26, countrysidetour=19, PPP=20, environmentalfactors=91, and percentage=0.0022.


## 5. Conclusion

In this research, the analytic hierarchy process is used, and the decision criterion distance between analytic hierarchy points is used as the reference value of the experimental model. Solve decision-making problems with many decision-making criteria and not so easy to quantify. It focuses on the decision-making system of the analytic hierarchy model in the structure which is more complex and maintains the stability of the system, models the application process with the complexity and diversity of the rural economy, collects sample data with the help of different types of rural tourism questionnaire surveys, and integrates the data of rural tourism and other tourism into the model. The following are obtained: (1) During the level analysis, each phenotype track uses RRM,C1=0.26, C2=0.223, C3=0.52, C4=0.25, C5=0.833, C6=0.442, C7=0.75, C8=0.127, C9=0.876, C10=0.792, C11=0.049, C12=0.16, C13=0.166, and C14=0.049. The problems of the complex structure of the evaluation can be divided into simple analysis modules, and each module is analyzed at a level. The phenotypic trajectory of each individual is divided into target layer, standard layer, and scheme layer. (2) Arrangement and decision modeling were performed according to one or several indicators of different factors. In the hierarchical random regression model, APC=0.214, UPUA=0.042, TO=0.081, YPUA=0.082, PCP=0.068, and APS=0.067. The characteristic quantity analysis of different environments can be carried out, and the amplitude error and frequency error obtained are relatively small. IAND=0.115, AVA=0.198, RD=0.119, PI=0.041, PCCL=0.142, IOC=0.201, and DSTC=0.069. The comparison shows that the hierarchical analysis model is better than the hierarchical random regression model. (3) High-efficiency hybrid model correlation acceleration is the worst model. The experimental data are APC=0.147, UPUA=0.029, TO=0.055, YPUA=0.06, PCP=0.047, APS=0.046, IAND=0.079, AVA=0.136, RD=0.082, PI=0.028, PCCL=0.098, IOC=0.139, and DSTC=0.048. (4) The predicted 2020 data and the actual data have small errors. The data obtained by the AHP model is GDP=1262.1, finance=185.09, budget=68, tax=51.92, fundbudget=69.23, transferincome=40.14, debtincome=7.73, disposablefinancialpower=177.37, fiscalexpenditure=191.26, publicbudget=88.68, governmentexpenditure=71.39, transferexpenditure=23.46, debtexpenditure=7.73, and lastyearbalance=2.39.



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*Source: 3997324-2022-03-12.xml*