Abstract: In order to challenge difficulty of the long-term cross-domain re-identification in deep person re-identification (ReID) with clothing changes, an interactive dual-stream learning (IDSL) algorithm model was proposed. Firstly, the clothing-agnostic modal images were generated from existing public datasets, and a dual-stream network with main and auxiliary branches was constructed to learn fine-grained features from the original images and modal images, respectively. Then a multi-scale feature cascade fusion module (MSFCF) was designed to reorganize the fine-grained features and introduce a cross-attention mechanism in jointly modeling for global semantics and local details, enhancing model robustness. And then a soft penalty batch normalization neck network (SoftBNNeck) was proposed to distinguish metric learning with classification learning, making the training model stable and controllable. Finally, a dual-stream consistency constraint loss (DCCLoss) was defined, and multi-loss joint training strategies were explored to better measure the probability distribution difference of cross-domain person identities, improving re-identification accuracy. Experiment results show that based on the challenging LTCC and Celeb-reID cross-domain person re-identification datasets, IDSL can achieve Rank-1/mAP scores of 73.8%/47.9% and 66.7%/22.6% respectively, outperforming prior methods in this field.