This paper proposes a dual enhanced network (DEN) based on channel and feature learning to address the problem of poor accuracy in cross- modality person re-identification (ReID) caused by heterogeneous sample differences, person occlusion, and background interference. At the channel level, visible channels are randomly swapped to explore the relationship between visible and infrared channels, enhancing the model's robustness to multimodal sample changes. At the feature level, a normalization-based attention module (NAM) is introduced before module sharing network to avoid noise interference on modality-invariant information learning by punishing weights with smaller contribution factors, and a feature separation module (FSM) is used to separate identity-related features from identity-independent features, improving the model's recognition ability for heterogeneous samples. Finally, the network is trained and supervised using hard sample triples and weighted regularization loss to constrain pedestrian feature learning. On the RegDB dataset, DEN achieves a high level of accuracy, with a Rank1 accuracy of 94.86% and mAP of 90.10%.