Cross-modality person re-identification， as a research hotspot in the field of computer vision， aims to solve the challenge of matching pedestrians across varying imaging conditions. Existing methods focus on extracting modality-shared features， but fail to fully mine the detailed features that are crucial for discriminative person identities. To address this issue， a hybrid convolutional enhancement and content-aware attention （HCECA） for cross-modality person re-identification is proposed， which aims to extract pedestrian features with more detailed information. First， a hybrid convolutional enhancement （HCE） module is embedded in the backbone network to capture richer cross-modality feature representation， enhancing the distinctiveness and robustness of the features. Second， a content-aware attention （CA） module is employed to mine rich detailed information， thereby improving the discriminability of pedestrian features. Finally， experiments are performed on the SYSU-MM01 and RegDB datasets. The proposed HCECA attains the Rank-1 accuracy of 72.21% and the mean average preeison（mAP） of 69.89% in the all-search mode on the SYSU-MM01 dataset， while achieving the Rank-1 accuracy of 92.23% and the mAP of 85.08% in the visible-infrared mode on the RegDB dataset. Both results outperform better than those of current cross-modality person re-identification methods.