基于数据驱动的旅游需求预测研究

合理预测景区客流量不仅可以为景区提供参考，更是旅游治理体系和治理能力现代化建设的内在要求。基于九寨沟风景区官网于2012年5月至2021年5月披露的每日客流量数据，运用Python爬取与九寨沟旅游相关的搜索行为数据和九寨沟每日平均气温，构建ARIMA、SVR模型和加入百度搜索指数与日平均气温的LSTM神经网络模型，对九寨沟风景区客流量进行拟合和预测。结果表明，LSTM神经网络模型预测精度高于ARIMA和SVR模型，加入百度搜索指数和日平均气温的LSTM神经网络模型可以显著提升客流量预测精度。

THE PREDICTION OF TOURISM DEMAND BASED ON DATA DRIVEN

Reasonable prediction of tourist flow in scenic spots can not only provide reference for scenic spots, but also is the inherent requirement of modernization construction of tourism management system and management capacity. Based on the daily passenger flow data disclosed by Jiuzhaigou scenic spot official website from May 2012 to May 2021, the search behavior data related to tourism in Jiuzhaigou and the daily average temperature were extracted, and the passenger flow of Jiuzhaigou scenic spot was fitted and predicted by constructing ARIMA model, SVR model and LSTM neural network model of adding factors. The results show that the prediction accuracy of LSTM neural network model is higher than ARIMA model and SVR model, and the LSTM neural network model with Baidu search index and daily average temperature can significantly improve the prediction accuracy of passenger flow.