With the improvement of material living standards, tourism has become an important part of people’s spiritual lives, and the demand for tourism has also grown rapidly. Rural tourism platforms serve as vehicles for disseminating unique cultures and play a significant role in the integration of culture and tourism. This study first selects Qiandongnan Prefecture as its research subject, utilizing an intelligent rural tourism service platform to analyze tourists’ behavioral patterns through their online travel diaries, thereby indirectly validating the effectiveness of the intelligent rural tourism service platform. In view of the shortcomings of LSTM that some tourism data will still be lost when the input sequence is too long, the attention mechanism is introduced on the basis of the SAE-LSTM model, and the Attention-SAE-LSTM prediction model is constructed, and the empirical research based on the tourist number dataset in Jiuzhaigou and Siguniang Mountain Scenic Area shows that the prediction effect of the Attention-SAE-LSTM model is better than that of AE-LSTM and SAE-LSTM models. Proving the prediction ability of the prediction model in the forecasting of tourism volume is conducive to the tourism sector to understand the distribution of tourist flow in advance.
