Deep　Multi-view　Graph　Clustering　(DMGC)　aims　to　partition　instances　into　different　groups　using　the　graph　information　extracted　from　multi-view　data.　The　mainstream　framework　of　DMGC　methods　applies　graph　neural　networks　to　embed　structure　information　into　the　view-specific　representations　and　fuse　them　for　the　consensus　representation.　However,　on　one　hand,　we　find　that　the　graph　learned　in　advance　is　not　ideal　for　clustering　as　it　is　constructed　by　original　multi-view　data　and　localized　connecting.　On　the　other　hand,　most　existing　methods　learn　the　consensus　representation　in　a　late　fusion　manner,　which　fails　to　propagate　the　structure　relations　across　multiple　views.　Inspired　by　the　observations,　we　propose　a　Structure-adaptive　Unified　gRaph　nEural　network　for　multi-view　clusteRing　(SURER),　which　can　jointly　learn　a　heterogeneous　multi-view　unified　graph　and　robust　graph　neural　networks　for　multi-view　clustering.　Specifically,　we　first　design　a　graph　structure　learning　module　to　refine　the　original　view-specific　attribute　graphs,　which　removes　false　edges　and　discovers　the　potential　connection.　According　to　the　view-specific　refined　attribute　graphs,　we　integrate　them　into　a　unified　heterogeneous　graph　by　linking　the　representations　of　the　same　sample　from　different　views.　Furthermore,　we　use　the　unified　heterogeneous　graph　as　the　input　of　the　graph　neural　network　to　learn　the　consensus　representation　for　each　instance,　effectively　integrating　complementary　information　from　various　views.　Extensive　experiments　on　diverse　datasets　demonstrate　the　superior　effectiveness　of　our　method　compared　to　other　state-of-the-art　approaches.　Copyright　©　2024,　Association　for　the　Advancement　of　Artificial　Intelligence　(www.aaai.org).　All　rights　reserved.