Multi-view　learning　has　attracted　considerable　attention　owing　to　its　capability　to　learn　more　comprehensive　representations.　Although　graph　convolutional　networks　have　achieved　encouraging　results　in　multi-view　research,　their　limitation　to　considering　only　nearest　neighbors　results　in　the　decrease　on　the　ability　to　obtain　high-order　information.　Many　existing　methods　acquire　high-order　correlation　by　stacking　multiple　layers　onto　the　model,　yet　they　could　lead　to　the　issue　of　over-smoothing.　In　this　paper,　we　propose　a　framework　termed　multi-scale　graph　diffusion　convolutional　network,　which　aims　to　gather　comprehensive　higher-order　information　without　stacking　multiple　convolutional　layers.　Specifically,　in　order　to　better　expand　the　receptive　field　of　the　node　and　reduce　the　parameter　complexity,　the　proposed　framework　utilizes　a　contractive　mapping　to　transform　features　from　multiple　views　on　decoupled　propagation　rules.　Our　framework　introduces　a　multi-scale　graph-based　diffusion　mechanism　to　adaptively　extract　the　abundant　high-order　knowledge　embedded　within　multi-scale　graphs.　Experiments　show　that　the　proposed　method　outperforms　other　state-of-the-art　methods　in　terms　of　multi-view　semi-supervised　classification.