With the development of deep learning, person re-identification (ReID) has been widely concerned and studied. At present, in practical application, there are three main problems in person ReID: first, it is difficult to locate the target person because the person is frequently partially occluded in crowed scenes; second, it is difficult to match the target person due to the similarity of the target person and other pedestrian features; third, the problem of model performance degradation caused by the large style discrepancies across domain/datasets. These three problems greatly limit the application of person ReID in real scenes. To solve these problems, we proposed a person ReID method based on effective features and self-optimized pseudo-label. Firstly, we designed a feature aggregation module which combines mask channel and pose channel to accurately extract the global saliency features, so as to solve the occlusion problem; secondly, we designed a head-shoulder feature auxiliary module to enhance the feature representation of the head-shoulder, so as to solve the problem of similarity between the target person and other pedestrian features; finally, we designed a self-optimized pseudo-label training module to improves the generalization ability of the model, so as to solve the problem of different styles in the cross-domain environment. Extensive contrast experiments with the state-of-the-art methods on multiple person re-ID datasets show that our method leads to significant improvement, which prove the effectiveness of our method.