RGB-Infrared person re-identification (Re-ID) is a challenging task which aims to match person images between visible and infrared modalities,playing a crucial role in criminal investigation and intelligent video surveillance.To address the weak feature extraction capability for fine-grained features in current cross-modal person Re-ID tasks,this paper proposes a person re-identification model based on fused attention and feature enhancement.First,automatic data augmentation techniques are employed to mitigate the differences in perspectives and scales among different cameras,and a cross-attention multi-scale Vision Transformer is proposed to generate more discriminative feature representations by processing multi-scale features.Then the channel attention and spatial attention mechanisms are introduced to learn information important for distinguishing features when fusing visible and infrared image features.Finally,a loss function is designed,which adopts the adaptive weight based hard triplet loss,to enhance the correlation between each sample and improve the capability of identifying different persons from visible and infrared images.Extensive experiments conducted on the SYSU-MM01 and RegDB datasets show that the proposed approach achieves mAP of 68.05% and 85.19%,respectively,outperforming many state-of-the-art approaches.Moreover,ablation experiments and comparative analysis validate the superiority and effectiveness of the proposed model.