In recent years, cross-modal pedestrian re-identification has gradually become one of the hotspots in the field of computer vision. However, it is crucial to effectively extract pedestrian features, further realize the interactive fusion of photos, and mine any potential relationships between pedestrian images while performing cross-modal pedestrian re-identification. To address this issue, a dual stream network based on channel grouping reorganization and attention mechanisms is proposed to extract more stable and rich features between the two modes. Specifically, to extract the shared characteristics of cross-modal images and to achieve the interactive fusion of modal information, the intra-modal feature channel grouping rearrangement module (ICGR) was inserted in the backbone network. Furthermore, to extract additional distinct local features, the possible association between pedestrian images captured using various modes was mined using the aggregated feature attention mechanism and cross-modal adaptive graph structure. A large number of experimental results on mainstream datasets such as SYSU-MM01 and RegDB demonstrate that the proposed algorithm has good generalization ability on multiple datasets. The cross-modal pedestrian re-identification algorithm achieves higher accuracy compared with the existing main algorithms.