Predicting the daily tourism flow of scenic spots is of great significance for improving the management quality and the tourist experience. Affected by complex factors, daily tourism flow data have strong nonlinear characteristics. In this article, a multilayer neural network S‐CNNLSTM is put forward to make accurate short‐term tourism flow prediction. First, to reduce the redundant information between the influencing factors, sparse principal component analysis is adopted to reduce the data dimension. Then the processed data is input into a deep neural network framework that combines the convolutional neural network (CNN) and long short‐term memory (LSTM) network. CNN extracts local trends, and LSTM is introduced to learn the inner law of time series and make prediction. Finally, through the experiments with real data and the comparison algorithms, the stability and practicability of the proposed method are verified.

中文翻译：

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使用SPCA和CNN-LSTM神经网络的每日游客流量预测

预测风景名胜区的日常旅游流量对于提高管理质量和游客体验具有重要意义。受复杂因素影响，日常旅游流量数据具有很强的非线性特征。本文提出了一种多层神经网络S‐CNNLSTM来进行准确的短期旅游流量预测。首先，为了减少影响因素之间的冗余信息，采用了稀疏主成分分析来减少数据量。然后，将处理后的数据输入到结合了卷积神经网络（CNN）和长短期记忆（LSTM）网络的深度神经网络框架中。CNN提取局部趋势，引入LSTM来学习时间序列的内在规律并进行预测。最后，