Correlation Between Intelligent Tourism Data Under Data Mining

With the vigorous development of technology, tourism data has shifted from traditional face-to-face teaching mode to electronic trading mode. How to make good use of electronic trajectory information related to travel is the most relevant issue for people. This article uses the ARIMA model (Autoregressive Integrated Moving Average Model) to predict the passenger flow in M city. It evaluated the correlation between the popularity of M city’s tourism industry network, weather conditions, and passenger numbers through grey correlation analysis. In the grey correlation analysis results of the relationship between network popularity and daily passenger flow, as the network popularity increases, daily passenger flow also keeps increasing. The network popularity value is 1 h, and the daily passenger flow value is 19963.