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

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, $\text{C}1=0.26$ , $\text{C}2=0.223$ , $\text{C}3=0.52$ , $\text{C}4=0.25$ , $\text{C}5=0.833$ , $\text{C}6=0.442$ , $\text{C}7=0.75$ , $\text{C}8=0.127$ , $\text{C}9=0.876$ , $\text{C}10=0.792$ , $\text{C}11=0.049$ , $\text{C}12=0.16$ , $\text{C}13=0.166$ , and $\text{C}14=0.049$ . 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, $\text{APC}=0.214$ , $\text{UPUA}=0.042$ , $\text{TO}=0.081$ , $\text{YPUA}=0.082$ , $\text{PCP}=0.068$ , and $\text{APS}=0.067$ . The characteristic quantity analysis of different environments can be carried out, and the amplitude error and frequency error obtained are relatively small. $\text{IAND}=0.115$ , $\text{AVA}=0.198$ , $\text{RD}=0.119$ , $\text{PI}=0.041$ , $\text{PCCL}=0.142$ , $\text{IOC}=0.201$ , and $\text{DSTC}=0.069$ . 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 $\text{APC}=0.147$ , $\text{UPUA}=0.029$ , $\text{TO}=0.055$ , $\text{YPUA}=0.06$ , $\text{PCP}=0.047$ , $\text{APS}=0.046$ , $\text{IAND}=0.079$ , $\text{AVA}=0.136$ , $\text{RD}=0.082$ , $\text{PI}=0.028$ , $\text{PCCL}=0.098$ , $\text{IOC}=0.139$ , and $\text{DSTC}=0.048$ . (4) The predicted 2020 data and the actual data have small errors. The data obtained by the AHP model is $\text{GDP}=1262.1$ , $\text{finance}=185.09$ , $\text{budget}=68$ , $\text{tax}=51.92$ , $\text{fund}\text{budget}=69.23$ , $\text{transfer}\text{income}=40.14$ , $\text{debt}\text{income}=7.73$ , $\text{disposable}\text{financial}\text{power}=177.37$ , $\text{fiscal}\text{expenditure}=191.26$ , $\text{public}\text{budget}=88.68$ , $\text{government}\text{expenditure}=71.39$ , $\text{transfer}\text{expenditure}=23.46$ , $\text{debt}\text{expenditure}=7.73$ , and $\text{last}\text{year}\text{balance}=2.39$ .