To address the problem that modal differences between different modal images in the process of cross-modal pedestrian re-recognition make it difficult to extract sufficient discriminative features, this paper proposes a color randomization data pre-processing method and designs a dual-stream multi-branch network model based on full relevant attention. The model uses ResNet-50 as the backbone network. Firstly, the input image is pre-processed with color randomization to improve the color style robustness of the model; a dual-stream network with non-shared modes of weight parameters is set in the shallow layer of the network for processing visible and infrared images respectively; secondly, full related attention is proposed to obtain the degree of association of different pixels from the spatial and channel dimensions to improve the model's ability to Finally, the multi-branch structure is used to extract multi-scale global features and local features to enhance the discriminative power of the extracted features. The experimental results of the cross-modal pedestrian re-identification method based on color randomization and full relevant attention on both the SYSU-MM01 and RegDB datasets are proved to be superior through comparative experiments.