This　work　aims　to　overcome　the　limitations　of　Euclidean　distance　weighted　sparse　subspace　clustering　that　does　not　consider　the　manifold　structure　of　a　rigid　body.　Here,　a　sparse　subspace　clustering　method　weighted　using　optical　flow　trajectory　manifold　topology　was　proposed.　In　the　proposed　algorithm,　the　manifold　distance　of　each　trajectory　in　the　space-time　similarity　adjacency　matrix　was　embedded　into　the　weight　matrix　to　solve　the　sparse　coefficient.　This　ensured　that　the　trajectory　with　a　relatively　closed　manifold　distance　became　the　sparse　self-expression　dictionary,　thereby　reducing　the　clustering　aliasing　error　of　synchronous　motion.　The　comparison　of　experiments　between　synchronous　motion　and　synchronous　swing　reveals　that　the　proposed　algorithm　can　reduce　aliasing　error　down　to　1%.　Finally,　the　motion　segmentation　results　of　Jacquard　needle　indicate　that　the　algorithm　can　be　potentially　used　for　industrial　applications.　©　2019,　Science　Press.　All　right　reserved.