Cross-modal person re-identification is a challenging pedestrian retrieval task. Existing research focuses on reducing inter-modal differences by extracting modal shared features, while ignoring the processing of intra-modal differences and background interference. In this regard, a mask reconstruction and dynamic attention (MRDA) network is proposed to eliminate the influence of background clutter by reconstructing the features of human body regions, thereby enhancing the robustness of the network on background changes. In addition, the dynamic attention mechanism is combined to filter irrelevant information, dynamically mine and enhance the discriminating feature representations, and eliminate the influence of intra-modal differences. The experimental results show that the probability the first search result matches successfully (Rank-1) and mean average precision (mAP) in the all-search mode of the SYSU-MM01 dataset reach 70.55% and 63.89%, respectively. The Rank-1 and mAP in the visible-to-infrared retrieval mode of the RegDB dataset reach 91.80% and 82.08%, respectively. The effectiveness of the proposed method is verified on the public datasets.