Considering the safety and comfort of vehicles during lane changing, a vehicle lane changing traffic conflict recognition model based on the improved PET algorithm and a lane changing behavior discrimination model based on an Eigen class (Eigen class) classifier are proposed. Firstly, micro-driving data are extracted from a publicly available dataset (City Sim) of urban expressway intersection area, and an improved Post - encroaching Time (PET) model is established to compute lane-changing features. Then, on the basis of correlation analysis, Eigen class classification method is used to identify the important characteristic variables of vehicle lane changing risk, and to construct a vehicle lane changing risk discrimination model with improved PET as the indicator. Finally, the accuracy of the lane-change risk discrimination model is evaluated by comparing and analyzing the classification prediction performance with that of K-nearest neighbor classification (KNN) and multiple support vector machine (MSV) algorithms. The results show that the discrimination accuracy of the lane change risk discrimination model is 99.73%, which is better than the KNN algorithm (99.71%), MSVM algorithm (99.50%) and DT algorithm (99.61%).