With　the　extensive　use　of　multi-view　data　in　practice,　multi-view　spectral　clustering　has　received　a　lot　of　attention.　In　this　work,　we　focus　on　the　following　two　challenges,　namely,　how　to　deal　with　the　partially　contradictory　graph　information　among　different　views　and　how　to　conduct　clustering　without　the　parameter　selection.　To　this　end,　we　establish　a　novel　graph　learning　framework,　which　avoids　the　linear　combination　of　the　partially　contradictory　graph　information　among　different　views　and　learns　a　unified　graph　for　clustering　without　the　parameter　selection.　Specifically,　we　introduce　a　flexible　graph　degeneration　with　a　structured　graph　constraint　to　address　the　aforementioned　challenging　issues.　Besides,　our　method　can　be　employed　to　deal　with　large-scale　data　by　using　the　bipartite　graph.　Experimental　results　show　the　effectiveness　and　competitiveness　of　our　method,　compared　to　several　state-of-the-art　methods.　IEEE