Cross-modality person re-identification (Re-ID), as a research hotspot in the field of computer vision, aims to solving the challenge of matching pedestrians across varying imaging conditions. Existing methods focus on extracting modality-shared features, but they 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. Specifically, 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. In addition, 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 mAP of 81.84% 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 those of current cross-modality person re-identification methods.