Dynamic Simulation and Comprehensive Quantitative Evaluation of Rural Tourism Core Competitiveness Based on Neural Network

Abstract

Rural tourism is the purpose of vacation tourism, which is an important support for rural economic development. Neural network algorithms are the second way to simulate human thinking. The purpose of this paper is to obtain benefits and diversify risks through dynamic simulation analysis and comprehensive quantitative analysis based on the neural network algorithm, so as to improve the core competitiveness of rural tourism. This paper first designs the neural network algorithm model, then analyzes the dynamic simulation model and comprehensive quantitative analysis, and then uses the artificial neural network algorithm to analyze and predict the core competitiveness of rural tourism. The results confirm the effectiveness of the algorithm. Under the demand forecast of the core competitiveness of rural tourism, using the artificial neural network algorithm, taking city A as an example, the number of tourists in rural tourism in city A from 2017 to 2021 was analyzed. The year with the largest number of people is 2021, with 2,586,000 people, and the year with the largest growth rate is 2019, with 2,576,900 people, a growth rate of 24.16%. Comparing the experimental data of each group, the results show that the algorithm has high efficiency in solving problems and also confirms the scientific validity of the algorithm.