Multi-view　learning　has　received　increasing　attention　in　recent　years　due　to　its　ability　to　leverage　valuable　patterns　hidden　in　heterogeneous　data　sources.　While　existing　studies　have　achieved　encouraging　results,　especially　those　based　on　graph　convolutional　networks,　they　are　still　limited　in　their　ability　to　fully　exploit　the　connectivity　relationships　between　samples　and　are　susceptible　to　noise.　To　address　the　aforementioned　limitations,　we　propose　a　framework　called　geometric　localized　graph　convolutional　network　for　multi-view　semi-supervised　classification.　This　framework　utilizes　a　diffusion　map　to　obtain　the　geometric　structure　of　the　feature　space　of　multiple　views　and　constructs　a　stable　distance　matrix　that　considers　the　local　connectivity　of　nodes　on　the　geometric　structure.　Additionally,　we　propose　a　truncated　diffusion　correlation　function　that　maps　the　distance　matrix　of　each　view　into　correlations　between　samples　to　obtain　a　reliable　sparse　graph.　To　fuse　the　features,　we　use　learnable　weights　to　concatenate　the　coordinates　of　the　geometric　structure.　Finally,　we　obtain　a　graph　embedding　of　the　fused　feature　and　topology　by　using　graph　convolutional　networks.　Comprehensive　experiments　demonstrate　the　superiority　of　the　proposed　method　over　other　state-of-the-art　methods.