Simulation and Analysis of Dynamic Trend of Tourism Big Data based on Feedback Constraint Association Rule 

Heqing Zhang1, Jiahao Xiang1, Leilei Wang2, Xiaobo Su3*

1Tourism College of Guangzhou University, Guangzhou, 510006, China

2Guangzhou Panyu Polytechnic, Guangzhou, 511483, China 

3Tourism College of Guangzhou University, Guangzhou,510006, China

Abstract: In present, with the rapid development of economy and science and technology, the tourism indus-try is leading the trend of development. Meanwhile, the era of big data is also one of the hot topics in media. How to apply the big data of tourism in the tourism industry is always one of the research focuses in the field of tourism. Aiming at the bottleneck problem of traditional data mining algorithm in big data processing, a dynamic trend mining method of tourism big data based on feedback association rule is proposed. With the proposed method, tourism data is collected, sorted, cleaned, filtered and statistically analyzed. The image specification and format of the data set are unified, and the feature extraction and fusion of the tourism image are carried out. The latent association information between tourism image features is mined by using feedback constrained association rule. The re-ranking of tourist images is carried out with the method of re-ranking based on graph model. Finally, the dynamic trend of tourism big data is analyzed according to the results of re-ranking. Through the use of massive real tourism data, the proposed method can achieve accurate and efficient analysis of the dynamic trend of tourism big data, which has certain economic value and practical significance for the development of the tourism industry.

Keywords: Tourism; Data; Association rule; Dynamic trend; Re-ranking