With　the　explosive　development　of　multi-view　data　from　diverse　sources,　multi-view　clustering　(MVC)　has　drawn　widespread　attention.　Existing　MVC　methods　still　have　several　limitations.　First,　it　is　difficult　to　sufficiently　consider　the　local　invariance　within　data　views.　Second,　view　fusion　usually　utilizes　weighted　averages,　thus　how　to　fuse　views　is　warranting　further　exploration.　Towards　these　two　issues,　this　paper　proposes　a　multi-channel　augmented　graph　embedding　convolutional　network　(MAGEC-Net)　for　multi-view　clustering　and　its　extended　end-to-end　model　(EMAGEC-Net).　The　proposed　frameworks　are　dedicated　to　exploring　the　consistency　and　complementarity　of　multi-view　data.　Specifically,　on　one　hand,　the　augmented　graphs　are　derived　from　generative　adversarial　networks,　which　explore　the　information　and　features　of　a　single　view　more　comprehensively.　On　the　other　hand,　each　augmented　view　is　considered　as　a　channel　and　fused　by　a　deep　fusion　network,　thus　this　method　effectively　improves　the　complementary　information　across　views.　Finally,　feature　extraction　is　performed　on　the　fused　consistent　graphs　to　enable　better　clustering.　Extensive　experiments　on　six　real　challenging　datasets　demonstrate　the　effectiveness　of　the　proposed　method　and　its　superiority　over　eight　compared　state-of-the-art　methods.