As　real-world　data　become　increasingly　heterogeneous,　multi-view　semi-supervised　learning　has　garnered　widespread　attention.　Although　existing　studies　have　made　efforts　towards　this　and　achieved　decent　performance,　they　are　restricted　to　shallow　models　and　how　to　mine　deeper　information　from　multiple　views　remains　to　be　investigated.　As　a　recently　emerged　neural　network,　Graph　Convolutional　Network　(GCN)　exploits　graph　structure　to　propagate　label　signals　and　has　achieved　encouraging　performance,　and　it　has　been　widely　employed　in　various　fields.　Nonetheless,　research　on　solving　multi-view　learning　problems　via　GCN　is　limited　and　lacks　interpretability.　To　address　this　gap,　in　this　paper　we　propose　a　framework　termed　Interpretable　Multi-view　Graph　Convolutional　Network　(IMvGCN).　We　first　combine　the　reconstruction　error　and　Laplacian　embedding　to　formulate　a　multi-view　learning　problem　that　explores　the　original　space　from　feature　and　topology　perspectives.　In　light　of　a　series　of　derivations,　we　establish　a　potential　connection　between　GCN　and　multi-view　learning,　which　holds　significance　for　both　domains.　Furthermore,　we　propose　an　orthogonal　normalization　method　to　guarantee　the　mathematical　connection,　which　solves　the　intractable　problem　of　orthogonal　constraints　in　deep　learning.　In　addition,　the　proposed　framework　is　applied　to　the　multi-view　semi-supervised　learning　task.　Comprehensive　experiments　demonstrate　the　superiority　of　our　proposed　method　over　other　state-of-the-art　methods.