Vehicle trajectory prediction can effectively reduce the collision risk caused by the sudden change of a vehicle trajectory, which is one of the key technologies to achieve safe driving. To address the problem that the traditional trajectory prediction algorithm lacks the analysis of the driver’s intention, this study proposes a vehicle trajectory prediction model that integrates generative adversarial networks and driving intention recognition. The model predicts vehicle trajectories under a generative adversarial network framework and introduces a deep neural network-based lane change intention recognition module to identify the driver’s lane change intention. A comparison test with LSTM, S-LSTM, CS-LSTM and S-GAN models is carried out on the public data set NGSIM. The experimental results show that compared with other trajectory prediction models, the CS-DNN-GAN model proposed in this study has better prediction accuracy.