With the development of the economy, high-quality free travel has become a mainstream leisure tourism method, and tourism-related information has also grown exponentially. Coupled with the diversity of information sources, tourist attraction consumers received a lot of fragmented information. Previous research pointed out that tourist attraction consumers' decision-making basis is increasingly relying on electronic word of mouth. However, the variety of reviews on the Internet makes it easier for tourist attraction consumers to make timely or even wrong judgments due to information integration errors. In order to solve the problems mentioned above, this research is based on big data text mining and sentiment analysis processing analysis, using the existing electronic travel review data to conduct mining analysis, in order to recommend the most useful review information to tourist attraction consumers, allowing tourist attraction consumers to make effective decisions. In other words, tourist attraction consumers can enable users to get advance reminders before making decision and presented with visualization. In this way, tourists who are consumers of tourist attractions can receive the information they need quickly and logically, and quickly make decision. Then, improve user satisfaction. Finally, results provide tourist attractions operators as a reference to improve and strengthen their core business contents and priorities.