To address the problem of limited discriminative power in existing person re-identification algorithms owing to the loss of details, a multi-level features cascade for person re-identification algorithm based on attention mechanism is proposed in this paper. First, the algorithm is used to cascade features at different depths to fully utilize the features of various levels and replenish detailed information in high-level feature maps. Then, a pair of complementary attention mechanism modules is introduced to integrate similar pixels and channels in the high-level feature maps, compensate for the space location information in the features, and improve the discriminativeness of the features. Finally, extensive experiments are performed on Market-1501, DukeMTMC-ReID, and CUHK03 data sets. Results show that the algorithm shows better recognition and average accuracies than most current mainstream algorithms.