In cloth-changing person re-identification tasks, clothing variation is a critical factor degrading recognition accuracy. To discover clothing-invariant features, a Positional Mask-Guided Model (PMGM) for Cloth-changing Person Re-identification was proposed. The PMGM model leverages four positional masks (head, upper body, lower body, and arms) to guide the network in capturing local fine-grained features, which are fused with global features to precisely extract clothing-invariant representations. During inference, integrating head feature matching with identity feature matching further enhances the model discriminative capability. Experimental results show that the PMGM model achieves 5.7% improvement in mAP and 6.1% improvement in Rank-1 on the PRCC dataset compared to baseline models.