Existing　multi-view　graph　learning　methods　often　rely　on　consistent　information　for　similar　nodes　within　and　across　views,　however　they　may　lack　adaptability　when　facing　diversity　challenges　from　noise,　varied　views,　and　complex　data　distributions.　These　challenges　can　be　mainly　categorized　into:　1)　View-specific　diversity　within　intra-view　from　noise　and　incomplete　information;　2)　Cross-view　diversity　within　inter-view　caused　by　various　latent　semantics;　3)　Cross-group　diversity　within　inter-group　due　to　data　distribution　differences.　To　this　end,　we　propose　a　universal　multi-view　consensus　graph　learning　framework　that　considers　both　original　and　generative　graphs　to　balance　consistency　and　diversity.　Specifically,　the　proposed　framework　can　be　divided　into　the　following　four　modules:　i)　Multi-channel　graph　module　to　extract　principal　node　information,　ensuring　view-specific　and　cross-view　consistency　while　mitigating　view-specific　and　cross-view　diversity　within　original　graphs;　ii)　Generative　module　to　produce　cleaner　and　more　realistic　graphs,　enriching　graph　structure　while　maintaining　view-specific　consistency　and　suppressing　view-specific　diversity;　iii)　Contrastive　module　to　collaborate　on　generative　semantics　to　facilitate　cross-view　consistency　and　reducing　cross-view　diversity　within　generative　graphs;　iv)　Consensus　graph　module　to　consolidate　learning　a　consensual　graph,　pursuing　cross-group　consistency　and　cross-group　diversity.　Extensive　experimental　results　on　real-world　datasets　demonstrate　its　effectiveness　and　superiority.