In　real-world　scenarios,　multi-view　data　comprises　heterogeneous　features,　with　each　feature　corresponding　to　a　specific　view.　The　objective　of　multi-view　semi-supervised　classification　is　to　enhance　classification　performance　by　leveraging　the　inherent　complementary　and　consistent　information　present　within　diverse　views.　Nevertheless,　many　existing　frameworks　primarily　focus　on　assigning　suitable　weights　to　different　views　while　neglecting　the　importance　of　consistent　information.　In　this　paper,　a　multi-view　semi-supervised　classification　framework　called　joint　diversity　and　consistency　graph　convolutional　network　(JDC-GCN)　is　proposed.　Firstly,　the　structure　of　graph　convolutional　network　is　introduced　to　the　multi-view　semi-supervised　classification,　capable　of　propagating　the　label　information　over　the　topological　structure　of　multi-view　data.　Secondly,　the　proposed　JDC-GCN　captures　the　complementary　and　consistent　information　from　multiple　views　through　two　indispensable　sub-modules,　Diversity-GCN　and　Consistency-GCN,　respectively.　Finally,　the　attention　mechanism　is　leveraged　to　dynamically　adjust　the　weights　of　various　views,　allowing　us　to　measure　the　significance　of　heterogeneous　features　and　the　consistent　graph　without　introducing　additional　parameters.　Comprehensive　experiments　on　eight　multi-view　datasets　are　conducted　to　validate　the　effectiveness　of　the　JDC-GCN　algorithm.　The　results　show　that　the　proposed　method　exhibits　superior　classification　performance　compared　to　other　state-of-the-art　methods.　©　The　Author(s),　under　exclusive　licence　to　Springer-Verlag　London　Ltd.,　part　of　Springer　Nature　2025.