Abstract:Air passenger travel is of great significance to the construction and operation of civil aviation airports. This paper studies the prediction of air passenger index, the main research work is as follows: Firstly, the air passenger index is defined, and the air passenger index is classified by K-means clustering method. Secondly, based on the principle of mutual information and correlation, the key influencing factors of air passenger index are selected, this work presents a method of MI-SVR (Mutual Information - Support Vector Regression) machine learning model based on mutual information , which estimates between the key influencing factors and air passenger index, this model(MI-SVR) is used to predict air passenger index. Finally, the model is validated by passenger throughput data of Shanghai Airport, the experimental results show that the MI-SVR model is feasible and effective, compared with classical models, IM-SVR model has better prediction effect. In addition, the experimental results also show that the prediction effect of each model is better after introducing influence factors based on mutual information. Overall, the study is helpful to the construction and operation of airport , At the same time, it can also help people choose the time to travel by air.