With the continuous development of information technology, the forecast of the recent development of things based on the network data has become a hotspot. In order to predict the monthly number of tourists in Beijing, this study established three kinds of single models with the search index of the relevant network keywords as independent variables:BP neural network, support vector regression, and random forest, and constructed a variety of combinatorial models to improve the prediction accuracy. The experimental results show that the combination of models based on GBDT have achieved higher prediction accuracy, the error is 3.16%. The forecast results can provide decision support for tourism management.