Abstract: Person re-identification (Re-ID), an important task in computer vision, aims to achieve precise matching of pedestrian identities across cameras. However, significant variations in clothing features under clothing-changing scenarios pose challenges to traditional Re-ID methods in feature extraction and information capture. To address this issue, a clothing-changing person Re-ID model named AMGA-ResNet50, integrating graph channel attention and multi-order gated residual network is proposed. First, in the adaptive graph channel attention (AGA) module, the graph convolution theory is introduced into the channel attention mechanism for clothing-changing scenarios, the channel dependency relationships are modeled as connections between feature nodes, and then the key identity features of clothing-changing pedestrians can be effectively captured through graph-structured processing. Second, the multi-order gated aggregation convolution (MOGA) module is employed to enhance the ability of feature information extraction through a multi-order gaming mechanism and gated aggregation strategy. Finally, an improved triplet loss (ITL) is proposed, which introduces an absolute distance constraint for positive sample pairs to reduce the distance between them. Experimental results show that the proposed model achieves Rank-1 and mAP scores of 67.3%/64.7%, 66.2%/19.0%, and 59.4%/20.7% on three public clothing-changing person Re-ID datasets, PRCC, Celeb-reID, and DeepChange, respectively, verifying the effectiveness of the model in clothing-changing scenarios.