Intelligent tourism information search behavior based on data mining

With the development of Internet technology and the progress of social economy, tourism has become the first choice for most people to take a vacation. However, the complicated information retrieval content presented by the Internet search engine has increased the difficulty of travel information search. To improve the targeting and accuracy of tourism information search behavior, data mining methods are used for analysis. Based on the characteristics of tourism text data, an improved clustering algorithm is introduced to classify the data, and a search method based on dynamic themes and search intentions is proposed. The results showed that the algorithm proposed in the study had good convergence and stability. The sum of squared errors curve results indicated that its error results in the three dimensions of traffic, catering, and accommodation were relatively small. The normalized discounted cumulative gain value of tourist attraction information search showed that the algorithm proposed in the study was significantly better than other comparative algorithms. Its maximum value approached 0.80 when the theme dimension was 5, and its accuracy in classifying information under both target search intention and analytical search intention exceeded 80%. The maximum time spent did not exceed 3 s. The main contribution of this study is to propose an innovative tourism information search method, which not only optimizes the processing and analysis process of tourism data, but also provides scientific basis for the improvement of tourism data recommendation systems. In addition, this method is of great significance for improving user experience and assisting users in making more informed travel decisions, promoting the advancement of tourism information search technology to a higher level.