Multi-view　clustering　has　attracted　significant　attention　in　recent　years　because　it　can　leverage　the　consistent　and　complementary　information　of　multiple　views　to　improve　clustering　performance.　However,　effectively　fuse　the　information　and　balance　the　consistent　and　complementary　information　of　multiple　views　are　common　challenges　faced　by　multi-view　clustering.　Most　existing　multi-view　fusion　works　focus　on　weighted-sum　fusion　and　concatenating　fusion,　which　unable　to　fully　fuse　the　underlying　information,　and　not　consider　balancing　the　consistent　and　complementary　information　of　multiple　views.　To　this　end,　we　propose　Cross-view　Fusion　for　Multi-view　Clustering　(CFMVC).　Specifically,　CFMVC　combines　deep　neural　network　and　graph　convolutional　network　for　cross-view　information　fusion,　which　fully　fuses　feature　information　and　structural　information　of　multiple　views.　In　order　to　balance　the　consistent　and　complementary　information　of　multiple　views,　CFMVC　enhances　the　correlation　among　the　same　samples　to　maximize　the　consistent　information　while　simultaneously　reinforcing　the　independence　among　different　samples　to　maximize　the　complementary　information.　Experimental　results　on　several　multi-view　datasets　demonstrate　the　effectiveness　of　CFMVC　for　multi-view　clustering　task.