To solve the problem that the pedestrian interaction model is difficult to be effectively constructed in the pedestrian trajectory prediction task

a trajectory prediction algorithm based on graph convolutional network (TP-GCN) was proposed to establish pedestrian interaction and predict future trajectories of pedestrians. First

the long short-term memory was used to extract the trajectory motion features of the trajectory sequences of pedestrians. Then

the pedestrians were considered as the nodes on the graph

and adjacency matrix was built to represent the created interactions. Next

the connection weights between unrelated nodes were screened out according to the blind zone. For trajectory motion features

the graph convolutional network was applied to extract the interactions between the trajectories and increase the extraction of the interaction in each trajectory

and the interaction was then encoded as trajectory interaction features by using long short-term memory. Furthermore

the weights of the graph convolutional network were optimized by the Deep Graph Info method to ensure that the motion pattern of individual accords with those of all the pedestrians in the scene. Finally

the trajectory motion features and trajectory interaction features were decoded using long short-term memory to complete the trajectory prediction. According to the experiment on the public datasets ETH and UCY

the proposed algorithm could make the predictions of pedestrian habits close to the real trajectories based on the interaction model between pedestrians

and the overall prediction accuracy was high. In addition

the ablation experiment and the visualization of the predicted trajectory also verified the effectiveness and interpretability of the algorithm.