The purpose of an infrared-visible person re-identification task is to match RGB and infrared images of the same identity. Because of the different imaging principles of the two modalities, it is difficult to efficiently extract discriminative modality-shared features. To address this issue, this study proposes a Modality-shared feature enhancement module and a global feature enhancement module that jointly extract enhanced discriminative global features. First, a modality-shared feature enhancement module is added to the backbone network to alleviate modality information and enhance modality-shared features with contextual information. Second, the global feature enhanced module encodes global features and jointly optimizes the loss function to further enhance the discriminative power of the global features while mining pattern features. Finally, the mutual mean learning method was used to reduce modality differences and constrain the feature representation. Experiments on mainstream datasets show that the proposed method achieves higher accuracy than existing methods.