Intelligent Tourism Information Search Behavior based on Data Mining

Smart tourism is of great significance in society, and its core is how to obtain and utilize tourism-related information efficiently to provide a better tourism experience. This paper proposes a data mining method based on the Apriori association rule algorithm to solve the difficult search problem for complex and diverse tourism information. During the process, operator data are used as the data source for data mining, and the Apriori association rule algorithm is used as the foundation to construct a smart tourism information search method. The method is constrained by the tourists’ travel order data at different tourist locations, and multithreaded parallel computing of the data is achieved through a parallel computing framework. The experimental results show that the initial accuracy of the proposed method in mining data types can reach up to 97.8%. When testing the number of association rules, the proposed method only had 2317 association rules with a support level of 0.032. The proposed method had a runtime of only 13.6Ks when involving 50M data pieces in large-scale datasets, which was lower than other methods. Hence, the proposed method can effectively search for smart tourism information and has high search efficiency and data accuracy.