Trajectory prediction plays a crucial role in autonomous driving tasks, as accurately and rapidly predicting the future trajectories of traffic participants can significantly enhance the safety and robustness of autonomous driving systems. This paper presents a novel trajectory prediction model that follows the encoder-decoder paradigm, achieving precise and rapid predictions of future vehicle trajectories by efficiently aggregating the spatiotemporal and interaction information of agents in traffic scenarios. We propose an agent-agent interaction information extraction module based on a sparse graph attention mechanism, which enables efficient aggregation of interaction information between agents. Additionally, we introduce a non-autoregressive query generation method that accelerates model inference speed by generating decoding queries in parallel. Comparative experiments with existing advanced algorithms show that our method improves the multimodal trajectory prediction metrics minADE, minFDE, and MR by an average of 9.1%, 11.8%, and 14.6%, respectively, while the inference time is only 33.7% of the average time taken by other algorithms. Finally, we demonstrate the effectiveness of the various modules proposed in this paper through ablation studies.