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In the context of "smart tourism" for large data applications, the Internet search engine records a large number of people searching for data. Compared with the official statistics, the search data has the characteristics of high efficiency and low cost. In this paper, we will explore and analyze the relationship between network search terms and scenic tourist numbers, analyze the theory of support vector machine in time series forecasting, and propose a support vector machine (SVM) algorithm for the forecast of scenic area passenger flow. The forecasting of the tourist flow rate of the local tourist area is carried out by the method of controlling the number of support vectors to reduce the calculation amount of the algorithm. Finally, the results show that the model has good prediction precision and can be used to predict the tourist attractions’ scenic passenger flow.

Scenic Spot Tourists Flow Prediction Research Based On Web Search Items