In this paper, a generative adversarial network GI-GAN that combines group interaction information with individual motion information is proposed. First, BiLSTM in the coding layer was used to extract the movement behavior of all pedestrians during the observation period. Second, based on a dual attention module, individual motion information and group interaction information having a high correlation with trajectory generation were calculated. Finally, using the generative adversarial network structure, global joint training was performed and the backpropagation error was obtained. Then, reasonable network parameters for each layer were obtained. Subsequently, the decoder used the acquired context information to generate multiple reasonable prediction trajectories. Experiment results show that compared with the S-GAN model, the average displacement error and absolute displacement error of the GI-GAN model are reduced by 8.8% and 9.2%, respectively, and the predicted trajectories have a higher accuracy and reasonable diversity.