To address the issue that existing person re-identification (Re-ID) algorithms have low robustness and discriminative capability when extracting pedestrian features with information loss, a novel Re-ID algorithm based on residual neural network is proposed for extracting multi-layer features of pedestrian images. During training phase, the residual network is used to extract the phase features after the four convolutional residual modules, to compensate for the information loss. And the triple loss function is used to supervise training of each feature vector. During the similarity measurement phase, the feature similarity is calculated according to the four feature vectors, the similarity of each stage is calculated by the summation of mapping function, and then the result of the summation is used to perform similarity matching. During the experiment, we validate the proposed algorithm on the Market-1501 and DukeMTMC-ReID datasets. The accuracy (Rank-1) of our algorithm reaches 91.7% and 84.9% and mean average precision (mAP) reaches 86.8% and 80.7%, respectively. Experimental results show that the multi-layer features extracted by our algorithm have considerable robustness and discriminative capability, which improves the accuracy of Re-ID.