Occluded　person　re-identification　poses　a　significant　challenge　due　to　the　obstruction　of　human　body　parts　by　various　obstacles,　requiring　the　use　of　incomplete　or　invisible　body　parts　for　matching.　Existing　methods　often　address　occlusion　through　feature　disentanglement.　However,　the　absence　of　occlusion　labels　hampers　accurate　disentanglement,　leading　to　challenges　in　precisely　distinguishing　occluded　regions.　In　response　to　these　challenges,　we　propose　a　novel　approach　that　actively　introduces　occlusion　to　guide　feature　disentanglement.　Our　method　incorporates　an　Occlusion　Sample　Augmentation　module,　actively　introducing　occlusion　to　person　images　and　generating　accurate　occlusion　labels.　Additionally,　we　employ　pose　estimation　to　partition　the　human　body　into　distinct　semantic　components,　enabling　selective　matching　of　non-occluded　parts.　To　address　interference　from　occlusion,　we　introduce　an　Occlusion-augmented　Push　Loss.　This　loss　function　effectively　separates　human　body　features　from　occluded　part　features　based　on　augmented　occlusion　location　labels,　guiding　the　learning　of　feature　disentanglement.　Experimental　results　on　both　the　Occluded-Duke　and　DukeMTMC-reID　datasets,　encompassing　occluded　and　holistic　re-identification　tasks,　showcase　the　outstanding　performance　of　our　method,　achieving　state-of-the-art　(SOTA)　performance.　©　2024　IEEE.