Research on Mining and Analyzing Tourists’ Consumption Habits in Tourism Management

Abstract

The rapid development of the tourism industry has led to a continuous change in the way of tourism consumption. This paper takes the information of tourists’ consumption behavior as the research object and investigates their consumption habits. This paper adopts the fuzzy clustering (FCM) algorithm to analyze tourists’ consumption habits and clustering validity indexes and then uses the association rule algorithm on the basis of the FCM algorithm to mine the factors affecting tourists’ consumption habits in tourism management. In this paper, tourists are divided into five categories: free youth, couples, parents and children, families, and explorers. In tourism consumption, the top three primary concerns of tourists are “attraction characteristics, safety and consumption, which account for 30.34%, 18.04% and 12.07%, respectively. In the process of tourists’ tourism consumption, 93.47% of the concern factors are attraction features, with a confidence level of 98.76% and rule enhancement of 1.245, respectively. In addition, the probability of security and consumption appearing in the text at the same time is high, with rule support, confidence level, and enhancement of 89.52%, 75.59%, and 1.045, respectively. Attention should be given to the characteristics of attraction, safety, consumption, and service simultaneously. The results of this paper help to identify tourists’ consumption preferences and provide suggestions for tourism management centers to accurately understand tourists’ consumption habits.

Keywords

• fuzzy clustering algorithm
• preference analysis
• association rule algorithm
• tourism consumption
• 03d78