Abstract
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.
Highlights
Nowadays, with the close integration between information technology and the tourism industry, the Internet, big data, and artificial intelligence have rapidly become the hot technologies of industrial development
Comparative Analysis of Prediction Performance of Each Model Algorithm. e comparative analysis is conducted on the spatiotemporal convolution neural network algorithm based on multifactor weighting to study the prediction performance of the constructed model on the spatial pattern of rural tourism. e constructed model is compared with long short-term memory (LSTM), Bi-directional long short-term memory (BiLSTM), VGGNet, AlexNet, and spatiotemporal graph convolution network (STGCN) from the perspective of accuracy, precision, recall, and F1 values, with the results shown from Figures 4 to 7
The neural network model has the highest precision, recall, and F1 value, and the F1 value may be smaller than the precision and recall rather than be between them. erefore, compared with the deep learning algorithm adopted by other scholars, the spatiotemporal CNN algorithm based on multifactor weighting has better prediction accuracy and better performance in evaluating the spatial pattern of rural tourism
Summary
With the close integration between information technology and the tourism industry, the Internet, big data, and artificial intelligence have rapidly become the hot technologies of industrial development. 3. Construction and Evaluation of Rural Tourism Spatial Pattern Based on Multifactor-Weighted Neural Network Algorithm
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