In-depth study of human driver's driving behavior and habits has significant implications for promoting the anthropomorphism of decision-making in intelligent vehicles and improving driving safety. For the typical scenario of highways, effective feature parameters that characterize lane-changing driving behavior were extracted based on the NGSIM dataset. The correlation between lane-changing driving behavior and driving parameters was analyzed, and driving behavior characteristics were quantified. A GMM-HMM-based lane-changing intention recognition model was established. The research results show that the model has a high recognition accuracy. The recognition accuracy of lane-changing behavior 1 second before the lane-changing point reaches 95.6%. The accuracy of recognizing lane-changing intentions exceeds 80% when there is the intention to change lanes. The model can be applied to the anthropomorphic design of intelligent vehicle lane-changing strategies, effectively reducing lane-changing risks and improving driving safety.