In　recent　years,　transformers　have　shown　strong　potential　in　occluded　person　re-identification　tasks,　but　existing　methods　still　face　key　challenges:　In　complex　occlusion　scenes,　the　global　processing　mechanism　of　traditional　Vision　Transformer　(ViT)　is　difficult　to　effectively　distinguish　between　target　features　and　occlusion　noise,　and　the　insufficient　utilization　of　low　layer　features　limits　the　robustness　of　the　model.　To　this　end,　this　paper　proposes　a　multi-layer　feature　fusion　framework　based　on　dynamic　token　compensation　and　cross-layer　feature　interaction　(DTC-CINet).　Firstly,　an　attention-driven　token　screening　compensation　module　is　designed,　which　divides　the　image　into　target　token　set　and　background　set　by　dynamic　weight　calculation,　and　uses　multi-layer　perception　to　transform　redundant　tokens　to　enhance　the　focus　ability　of　the　model　on　key　areas.　Secondly,　a　cross-layer　feature　interaction　module　is　proposed　to　fuse　the　low-level　location　information　and　high-level　semantic　features　by　constructing　an　attention　mapping　network　to　improve　the　reasoning　ability　of　occlusion　areas.　Tests　reveal　that　the　suggested　method　is　significantly　better　than　the　current　methods　on　the　mainstream　data　sets,　especially　in　the　occlusion　scene　shows　stronger　robustness.　©　2025　IEEE.