Smart Tourist Attraction Data Mining Based on FP-Growth Algorithm

Smart tourism is the application of new information and communication technology, through mobile phones and other smart devices to obtain tourism information or play relevant information, so that tourists can more conveniently access to scenic spot information. Based on the association rules mining algorithm, the data generated by the tourism industry and tourist attractions can be comprehensively mined to realize emotional tendency analysis and personalized recommendation, and enhance tourist experience. Based on data mining technology, this paper constructs an association rule mining model, analyzes the advantages of FP-Growth algorithm, and designs the flow of FP-Growth algorithm. According to the steps of “reading initial data, calculating support, arranging in descending order by frequency, reordering initial data, building FP-tree, mining frequent item set”, this paper completes the data mining example of smart tourism attractions, and verifies the superiority of FP-Growth algorithm.