In the process of applying deep learning to intrusion detection, in order to ensure the recognition accuracy of the model, a large number of data sets need to be classified manually, and then the model training is carried out after labeling. In practice, the efficiency of manual label designation for enough data sets is extremely low. This paper aims at the experimental data set encountered in the intrusion detection of intelligent network vehicles The problem of classification difficulty is proposed, and a method of vehicle intrusion detection based on Generative Adversarial Networks is proposed. Firstly, the vehicle driving data is collected, the collected data are put into the Generative Adversarial Networks for data classification, and the data set after training classification is used for model training. The experimental results show that the data classification can effectively improve work efficiency and reduce the resource overhead, which is practical in the application field.