The investigation is about accurately predicting the tourist number in tourist season. Factors that affecting people's travel demand is currently more and more, and the impact on the tourist number in tourist season is increasingly complex. The traditional prediction models of tourist number in tourist season are mostly static model, with the property weight of to each determine factors are relatively fixed, cannot be flexible changed. But these attributes influence the final number of visitors differently, which led to the traditional model is difficult to form an accurate judgment. In order to avoid these shortcomings, the analysis method based on big data presented in the tourist number prediction in tourist season. Relevant data are collected, to make pre-processing, including data cleaning, data transformation and data integration. Based on the theory of support vector machine, the data attributes are classified, the prediction model of number of visitors in the tourist season is established. By calculating the weight of model attribute classification results, the classification of importance degree of tourist number affected by different properties is obtained. By comparison with the parameters, the forecast results of tourist number in tourist season are obtained. The results show that using the proposed algorithm in this article, the prediction of the tourist number in tourist season can be effectively improved the accuracy, thus providing an accurate basis for decision-making tourism industry and promoting the development of tourism industry more healthy, rapid and sustainable.
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