Multi-view　data　containing　complementary　and　consensus　information　can　facilitate　representation　learning　by　exploiting　the　intact　integration　of　multi-view　features.　Because　most　objects　in　the　real　world　often　have　underlying　connections,　organizing　multi-view　data　as　heterogeneous　graphs　is　beneficial　to　extracting　latent　information　among　different　objects.　Due　to　the　powerful　capability　to　gather　information　of　neighborhood　nodes,　in　this　article,　we　apply　Graph　Convolutional　Network　(GCN)　to　cope　with　heterogeneous　graph　data　originating　from　multi-view　data,　which　is　still　under-explored　in　the　field　of　GCN.　In　order　to　improve　the　quality　of　network　topology　and　alleviate　the　interference　of　noises　yielded　by　graph　fusion,　some　methods　undertake　sorting　operations　before　the　graph　convolution　procedure.　These　GCN-based　methods　generally　sort　and　select　the　most　confident　neighborhood　nodes　for　each　vertex,　such　as　picking　the　top-k　nodes　according　to　pre-defined　confidence　values.　Nonetheless,　this　is　problematic　due　to　the　non-differentiable　sorting　operators　and　inflexible　graph　embedding　learning,　which　may　result　in　blocked　gradient　computations　and　undesired　performance.　To　cope　with　these　issues,　we　propose　a　joint　framework　dubbed　Multi-view　Graph　Convolutional　Network　with　Differentiable　Node　Selection　(MGCN-DNS),　which　is　constituted　of　an　adaptive　graph　fusion　layer,　a　graph　learning　module,　and　a　differentiable　node　selection　schema.　MGCN-DNS　accepts　multi-channel　graph-structural　data　as　inputs　and　aims　to　learn　more　robust　graph　fusion　through　a　differentiable　neural　network.　The　effectiveness　of　the　proposed　method　is　verified　by　rigorous　comparisons　with　considerable　state-of-the-art　approaches　in　terms　of　multi-view　semi-supervised　classification　tasks,　and　the　experimental　results　indicate　that　MGCN-DNS　achieves　pleasurable　performance　on　several　benchmark　multi-view　datasets.