Spatial Pattern Evaluation of Rural Tourism via the Multifactor-Weighted Neural Network Model in the Big Data Era

The exploration of the evaluation effect of rural tourism spatial pattern based on the multifactor-weighted neural network model in the era of big data aims to optimize the spatial layout of rural tourist attractions. There are plenty of problems such as improper site selection, layout dispersion, and market competition disorder of rural tourism caused by insufficient consideration of planning and tourist market. Hence, the multifactor model after simple weighting is combined with the neural network to construct a spatiotemporal convolution neural network model based on multifactor weighting here to solve these problems. Moreover, the simulation experiment is conducted on the spatial pattern of rural tourism in the Ningxia Hui Autonomous Region to verify the evaluation performance of the constructed model. The results show that the prediction accuracy of the model is 97.69%, which is at least 2.13% higher than that of the deep learning algorithm used by other scholars. Through the evaluation and analysis of the spatial pattern of rural tourist attractions, the spatial distribution of scenic spots in Ningxia has strong stability from 2009 to 2019. Meanwhile, the number of scenic spots in the seven plates has increased and the time cost of scenic spot accessibility has changed significantly. Besides, the change rate of the one-hour isochronous cycle reaches 41.67%. This indicates that the neural network model has high prediction accuracy in evaluating the spatial pattern of rural tourist attractions, which can provide experimental reference for the digital development of the spatial pattern of rural tourism.

Automated summary

The study explores the evaluation effect of rural tourism spatial pattern using the multifactor-weighted neural network model, finding high prediction accuracy and strong stability, which can provide experimental reference for the digital development of rural tourism spatial pattern.