This paper introduces a novel framework for partial person re-identification, addressing the challenge of image spatial misalignment due to occlusions. The framework utilizes an adaptive threshold-guided masked graph convolutional network and incorporates human attributes to enhance the accuracy of pedestrian representations. Experimental results demonstrate its effectiveness across multiple public datasets.