Aiming　at　the　problem　of　low　efficiency　and　insufficient　accuracy　of　threshold　solution　in　multithreshold　sculpture　image　segmentation,　this　paper　proposes　a　threshold　segmentation　algorithm　for　sculpture　images　based　on　sparse　decomposition.　In　this　paper,　sparse　decomposition　is　introduced　to　optimize　the　model　to　reduce　the　impact　of　local　noise　on　segmentation　accuracy,　and　an　energy　functional　based　on　pixel　coconstraint　is　built　to　make　up　for　the　defect　that　pixels　cannot　retain　local　details.　At　the　same　time,　the　weighted　sum　of　elite　solution　sets　is　used　to　determine　Neighborhood　centers　increase　communication　between　groups.　Experiments　show　that　compared　with　other　algorithms,　the　above　method　has　significant　advantages　in　convergence　efficiency　and　accuracy.