In pedestrian re-identification (Re-ID) tasks, insufficient feature discriminability, poor training stability, and weak robustness are often caused by viewpoint changes, occlusion, and background interference. A multi-strategy pedestrian re-identification network with enhanced robustness was proposed. Triplet Attention modules were embedded in the four stages of the ResNet50 backbone network. Channel, horizontal, and vertical attention mechanisms were used. Feature weights were dynamically adjusted. The focusing ability on key pedestrian areas was enhanced. Background noise was suppressed. A constraint term on the ratio of positive and negative sample distances was introduced on the basis of traditional Triplet Loss. The feature space distribution was finely regulated. Intra-class compactness was improved. Inter-class discriminability was enhanced. Kaiming initialization and normalization layer optimization strategies were adopted. Gradient instability in the early training stage was reduced. Network convergence was accelerated. Feature representation ability was enhanced. The mAP of the method is 88.4%, 78.9%, and 54.6% on the Market1501, DukeMTMC-ReID, and MSMT17_V2 datasets. The Rank-1 index is 95.3%, 88.9%, and 78.1%. These results are superior to those of existing mainstream methods. The visualization of heat maps and ranking charts shows that pedestrian details are accurately captured. Strong adaptability to complex scenes is achieved. This method provides an effective solution to improve the robustness of cross-camera pedestrian matching.