In order to solve the problem of the spatial dislocation caused by person detection error in person re-identification, the local-based deep neural networks model only learn the adjacent local relationship, resulting in lack of long-distance local correlation. This paper proposes a person re-identification algorithm based on first-order and second-order spatial information. On the backbone network, first-order spatial mask is learned to fine-tune the spatial weight of the input image to reduce the background interference. The second-order spatial mask is used to model the long-distance dependency relationship, and local features are integrated into the dependency model to obtain the global feature representation. In the local branch, DropBlock is introduced to regularize the pedestrian features to avoid the network model relying too much on specific part features. In the training stage, the whole network is optimized by the label-smoothed cross-entropy loss and the triple loss with positive samples’ center. Experimental results based on Market-1501 and DukeMTMC-reID data sets show that compared with other mainstream algorithms, the person re-identification accuracy of the algorithm is higher, and the extracted pedestrian features are more discriminative and robust.