Occluded person re-identification (re-id) is a task in pedestrian retrieval where occluded person images are matched with holistic person images. Most methods leverage semantic cues from external models to align the availability of visible parts in the feature space. However, presenting visible parts while discarding occluded parts can lead to the loss of semantics in the occluded regions, and in severely crowded regions of occlusion, it will introduce inaccurate features that pollute the overall person features. Thus, constructing person features for occluded regions based on the features of its holistic parts has the potential to address the above issues. In this work, we propose a pose-guided node and trajectory construction transformer (PNTCT). The part feature extraction module extracts parts feature of the person and incorporates pose information to activate key visible local features. However, this is not sufficient to completely separate occluded regions. To further distinguish visible and occluded parts, the skeleton graph module adopts a graph topology to represent local features as graph nodes, enhancing the network’s sensitivity to local features by constructing a skeleton feature graph, which is further utilized to weaken the occlusion noise. The node and trajectory construction module (NTC) mines the relationships between skeleton nodes and aggregates the information of the person’s skeleton to construct a novel skeleton graph. The features of the occluded regions can be reconstructed via the features of the corresponding nodes in the novel skeleton graph. Extensive experiments and analyses confirm the effectiveness and superiority of our PNTCT method.