Visible-Infrared person re-identification (VI-ReID) is a task of matching person in both visible and infrared modes. Although existing VI-ReID methods have achieved certain results, they have not effectively utilized the cross modality significant information and multi-granularity information of person. Therefore, this paper proposes a VI-ReID method for modal significant consistency feature mining and multi-granularity feature enhancement. Among them, the modal significant consistency feature mining improves the relationship weight selection method of cross attention mechanism, achieving effective extraction of modal significant consistency features; multi-granularity feature enhancement effectively utilizes the local discriminative information of person by extracting fine-grained features of person at different granularity. Compared with existing methods, the method proposed in this paper has achieved significant advantages on the public datasets SYSU-MM01 and RegDB. Experiments have shown that the effective utilization of cross modality significant information and multi-granularity information can enhance the cross modal robustness of features.