Aiming at the problem that the existing deep learning based pedestrian re-identification methods lack pedestrian discriminative feature information caused by pedestrian posture change and partial occlusion, a pedestrian re-identification network model based on deep feature fusion is proposed. Firstly, the convolution layer and the pooling layer are used to extract the deep features of the network many times to improve the network performance from the spatial dimension. Secondly, in order to improve the generalization ability of the model, a batch normalization layer is added after the deep fusion features. Finally, the label smoothing loss function and the triple loss function are used to train the model. The experimental results show that the proposed deep feature fusion method has good expression ability. The proposed method is validated on four datasets: Market1501, Duke MTMC-reID, CUHK03 and MSMT17. On Market1501, Rank-1 and mAP reach 95.0% and 85.6%.