Accurate prediction of tourism demand is critically important for the efficient allocation of resources in scenic areas and managing sudden events. This paper presents a new tourism demand prediction model, PCA-BPNN neural network model. It utilizes Principal Component Analysis (PCA) to reduce the dimensionality of the collected Baidu Index data and mitigate overfitting issues. The model then constructs a backpropagation neural network (BPNN). Empirical research demonstrates that PCA-BPNN effectively identifies the nonlinear relationship between search keywords and the number of tourist arrivals and outperforms all benchmark models in terms of predictive performance.

Xin Zhao. Tourism demand forecasting using PCA-BPNN. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 9: 72-80. https://doi.org/10.25236/AJCIS.2023.060911.

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