Modern　social　networks　contain　various　types　of　objects　and　rich　interactive　information,　the　effect　of　traditional　detection　methods　is　limited.　Many　researchers　proposed　graph　neural　network　methods　to　solve　this　problem,　but　most　of　them　have　ignored　the　heterogeneous　nature　of　social　networks.　So,　we　proposed　a　new　model　named　Multi-view　Similarity-based　Graph　Convolutional　Network(MSGCN)　to　address　this　problem.　Our　model　adopted　a　multi-view　method　to　deal　with　rich　interaction　and　heterogeneous　graph　data.　Firstly,　we　converted　the　heterogeneous　graph　into　many　single-views　by　using　meta-　path.　Then,　we　employed　a　similarity-based　graph　convolutional　network　to　learn　node　representation　for　every　single-　view.　Finally,　the　multiple　views　features　were　aggregated　together　through　the　attention　mechanism　to　generate　the　final　representation.　Experimental　results　show　that　our　MSGCN　is　better　than　other　network　representation　learning　baselines.　©　2021　IEEE.