Network　embedding　has　been　an　effective　tool　to　analyze　heterogeneous　networks　(HNs)　by　representing　nodes　in　a　low-dimensional　space.　Although　many　recent　methods　have　been　proposed　for　representation　learning　of　HNs,　there　is　still　much　room　for　improvement.　Random　walks　based　methods　are　currently　popular　methods　to　learn　network　embedding;　however,　they　are　random　and　limited　by　the　length　of　sampled　walks,　and　have　difficulty　capturing　network　structural　information.　Some　recent　researches　proposed　using　meta　paths　to　express　the　sample　relationship　in　HNs.　Another　popular　graph　learning　model,　the　graph　convolutional　network　(GCN)　is　known　to　be　capable　of　better　exploitation　of　network　topology,　but　the　current　design　of　GCN　is　intended　for　homogenous　networks.　This　paper　proposes　a　novel　combination　of　meta-graph　and　graph　convolution,　the　meta-graph　based　graph　convolutional　networks　(MGCN).　To　fully　capture　the　complex　long　semantic　information,　MGCN　utilizes　different　meta-graphs　in　HNs.　As　different　meta-graphs　express　different　semantic　relationships,　MGCN　learns　the　weights　of　different　meta-graphs　to　make　up　for　the　loss　of　semantics　when　applying　GCN.　In　addition,　we　improve　the　current　convolution　design　by　adding　node　self-significance.　To　validate　our　model　in　learning　feature　representation,　we　present　comprehensive　experiments　on　four　real-world　datasets　and　two　representation　tasks:　classification　and　link　prediction.　WMGCN＇s　representations　can　improve　accuracy　scores　by　up　to　around　10%　in　comparison　to　other　popular　representation　learning　models.　What＇s　more,　WMGCN＇feature　learning　outperforms　other　popular　baselines.　The　experimental　results　clearly　show　our　model　is　superior　over　other　state-of-the-art　representation　learning　algorithms.