The　domain　of　multi-view　semi-supervised　classification　is　an　appealing　topic　in　real-world　applications.　Due　to　the　powerful　capability　of　gathering　information　from　neighbors,　Graph　Convolutional　Network　(GCN)　has　become　a　hotspot　in　the　classification　task.　However,　most　of　multi-view　classification　works　based　on　GCN　only　assign　weights　for　feature　fusion,　and　directly　consider　the　weighted　sum　of　the　adjacency　matrices,　ignoring　the　interaction　and　correlation　among　features.　These　may　be　problematic　since　aggregating　the　matrices　from　less　relevant　views　may　destroy　the　original　topology　space,　leading　to　undesired　performance.　To　tackle　the　aforementioned　challenges,　this　paper　presents　an　Adaptive　Multi-Channel　Graph　Convolutional　Network　(AMC-GCN).　To　extract　the　interactive　information,　AMC-GCN　designs　a　deep　interactive　feature　integration　network　to　incorporate　consensus　and　complementary　information.　To　fuse　the　graph　structures,　AMC-GCN　exploits　the　relevance　between　views　and　imposes　an　adjacency　matrix　fusion　network　on　constructing　multiple　GCN　channels,　thereby　delivering　discriminative　information　on　graphs.　To　enhance　the　homogeneity　of　the　framework,　AMC-GCN　applies　a　con-trastive　loss　to　joint　learning　during　the　optimization　for　classification.　With　these　considerations,　AMC-GCN　exploits　relevant　and　interactive　information　between　views　to　promote　graph　and　feature　fusion.　Substantial　experimental　results　on　real-world　datasets　verify　the　superiority　of　AMC-GCN.