Pedestrian trajectory prediction can effectively reduce the collision risk caused by sudden changes in pedestrian trajectory, which has been widely used in intelligent transportation and monitoring systems. At present, most of the existing researches use undirected graph convolution network to model the social interaction between pedestrians. This method lacks the consideration of the relevance of the hidden state of pedestrians, and is prone to generate redundant interactions between pedestrians. To solve this problem, a pedestrian trajectory prediction model based on attention mechanism and sparse graph convolution (DASGCN) is proposed. By constructing a deep attention mechanism, the association of motion hiding states among pedestrians is captured, and the pedestrian motion state features are accurately extracted. Self-adjusting sparse method is further proposed to reduce the motion trajectory deviation caused by redundant information and solve the problem of dense and undirected pedestrian interaction. The proposed model was verified on ETH and UCY datasets, and the average displacement error (ADE) and final displacement error (FDE) reached 0.36 and 0.63 respectively. The experimental results show that DASGCN is superior to traditional algorithms in predicting pedestrian trajectory.