In order to reduce the probability of accidents caused by bad lane changing behavior, it is necessary to identify the lane changing behavior during the actual driving of the vehicle. In this paper, the IOS intelligent device is used to collect data, and the corresponding feature vector is established. The vehicle lane change behavior recognition model based on support vector machine is proposed. An improved N-δ sliding window interception algorithm is proposed for the recognition of continuous lane change behavior so as to divide the data containing multiple behaviors quickly, the sample data is used to verify the feasibility of the N-δ sliding window interception algorithm and the validity of the classifier.