Due to the interference of various factors in the complex situation of reality, the errors may occur in the person re-identification. To improve the accuracy of person re-identification, a person re-identification algorithm based on spatial-temporal regularization was proposed. Firstly, the ResNet-50 network was used to extract the features of the input video sequence frame by frame, and the series of frame-level features were input into the spatial-temporal regularization network to generate corresponding weight scores. Then the weighted average was performed on the frame-level features to obtain the sequence-level features. To avoid weight scores from being aggregated in one frame, frame-level regularization was used to limit the difference between frames. Finally, the optimal results were obtained by minimizing the losses. A large number of tests were performed on MARS and DukeMTMC-ReID datasets. The experimental results show that the mean Average Precision (mAP) and the accuracy can be effectively improved by the proposed algorithm compared with Triplet algorithm. And the proposed algorithm has excellent performance for human posture variation, viewing angle changes and interference with similar appearance targets.