Self-representation　model　has　made　good　progress　for　a　single　view　subspace　clustering.　This　paper　proposed　the　multi-view　subspace　clustering　model　based　on　self-representation.　This　model　assumes　that　the　samples　from　different　classes　are　embedded　in　independent　subspaces.　Thus,　the　fused　multi-view　self-representation　feature　should　be　block　diagonal,　and　a　block　diagonal　regularizer　with　the　complementarity　of　multi-view　information　is　given.　The　model　optimization　algorithm　by　alternating　minimization　is　proposed　and　its　convergence　without　any　additional　assumption　is　proved.　With　the　complementarity　of　multi-view　information　and　the　block　diagonal　property,　our　model　will　depict　data　more　comprehensively　than　single　view　independently.　The　extensive　experiments　on　public　datasets　demonstrate　the　effectiveness　of　our　proposed　model.