Objective Aiming at the problem of insufficient attention to the delicate area of pedestrians and the vulnerability of the network to noise in the current cross-modal person re-identification research this paper proposed a multi-branch fusion variational refinement distillation learning method. Methods Firstly the network aggregated global features of different granularity through multiple branches urging the deep network to learn the global information and details of the two modes to enrich the feature descriptors of pedestrians. Then combined with the variational refinement distillation strategy the feature information was recompressed the deep information related to the task was retained and the useless interferences were discarded. Finally the different features captured by the network were jointly supervised by multiple loss functions to improve the sensitivity of the network to pedestrian representation. Results R-1 and mAP reached 66. 93% and 65. 25% respectively with the proposed method in the full search mode of the SYSU-MM01 dataset the R-1 and mAP reached 78. 26% and 77. 83% respectively in the visible to infrared setting of the RegDB dataset. Conclusion Through ablation experiments comparative experiments and visualization experiments the effectiveness of the proposed method is fully verified.