Facing　the　increasing　heterogeneity　of　data　in　the　real　world,　multi-view　learning　has　become　a　crucial　area　of　research.　Graph　Convolutional　Networks　(GCNs)　are　powerful　for　modeling　both　graph　structures　and　features,　making　them　a　focal　point　in　multi-view　learning　research.　However,　these　methods　typically　only　account　for　static　data　dependencies　within　each　view　separately　when　constructing　the　topology　necessary　for　GCNs,　overlooking　potential　relationships　across　views　in　multi-view　data.　Furthermore,　there　is　a　notable　absence　of　theoretical　guidance　for　constructing　multi-view　data　topologies,　leading　to　uncertainty　regarding　the　progression　of　graph　embeddings　toward　a　consistent　state.　To　tackle　these　challenges,　we　introduce　a　framework　named　energy-constrained　multi-view　graph　diffusion.　This　approach　establishes　a　mathematical　correspondence　between　multi-view　data　and　GCNs　via　graph　diffusion.　It　treats　multi-view　data　as　a　unified　entity　and　devises　a　feature　propagation　process　with　inter-view　awareness　by　considering　both　inter-view　and　intra-view　feature　flow　across　the　entire　system.　Additionally,　an　energy　function　is　introduced　to　guide　the　inter-　and　intra-view　diffusion,　ensuring　that　the　representations　converge　towards　global　consistency.　The　empirical　research　on　several　benchmark　datasets　substantiates　the　benefits　of　the　proposed　method.　©　2024　ACM.