Knowledge　Graph　provides　an　effective　scheme　for　unstructured　knowledge　on　the　Internet.　but,　to　a　large　extent,　the　lack　of　information　in　the　knowledge　graph　restricts　the　application　of　the　knowledge　graph.　Therefore,　knowledge　graph　completion　has　been　an　attractive　research　area.　In　recent　years,　many　researchers　apply　graph　convolutional　neural　networks　to　knowledge　graph　embedding　so　as　to　solve　this　problem,　including　R-GCN,　SACN　et　al.　The　current　state-of-the-art　SACN　uses　graph　convolutional　neural　as　an　encoder　to　make　more　accurate　embeddings　of　graph　nodes,　and　uses　a　convolutional　network　as　a　decoder,　leading　a　good　performance　of　link　prediction　task.　In　this　work,　we　produce　a　novel　graph　representation　model　based　on　SACN　-　-property　graph　convolutional　network　called　PGCN.　PGCN　treats　the　knowledge　graph　as　a　property　graph,　regarding　the　initial　embedding　vector　of　entities　and　relations　as　the　property　of　nodes　and　edges.　What　makes　the　model　different　is　that　it　introduces　the　node　clustering　before　convolving　the　nodes,　so　that　the　graph　takes　into　account　the　importance　of　neighbors　when　aggregating　the　neighbors　of　nodes.　We　adopt　the　probability　model　Conv-TransE　proposed　by　SACN　for　the　modeling　of　relation　of　graph,　Conv-TransE　takes　advantages　of　ConvE　and　use　probability　method　which　greatly　improves　the　efficiency　of　the　experiment　and　avoids　the　construction　of　the　corrupted　triplet.　We　conduct　experiments　on　the　standard　datasets　WN18RR　and　FB15k-237,　and　demonstrate　that　our　new　model　achieves　better　performance.　©　Published　under　licence　by　IOP　Publishing　Ltd.