As　the　last　step　in　the　process　of　knowledge　graph　construction,　knowledge　graph　completion　predicts　and　inferences　new　factual　triples　according　to　the　existing　knowledge,　which　is　of　great　significance　to　the　downstream　application　of　the　knowledge　graph.　The　knowledge　graph　completion　technology　learns　representations　of　entities　and　relations　in　the　knowledge　graph　and　reconstructs　triples　factual　relations　in　the　representation　space　to　measure　their　effectiveness,　so　as　to　predict　and　supplement　entities　and　relations.　At　present,　Graph　Convolutional　Network　(GCN)　is　widely　used　in　entity　modeling,　which　combines　the　graph　structure　information　to　learn　the　vector　representation　of　entities.　However,　most　algorithms　only　focus　on　static　knowledge　graph　completion,　ignoring　the　important　role　of　time　in　the　knowledge　graph,　and　the　modeling　of　entities　is　not　comprehensive　enough.　In　this　paper,　a　knowledge　graph　completion　based　on　the　joint　embedding　of　entities　is　proposed,　which　combines　structural　feature　embedding　of　the　graph　and　dynamic　embedding　of　entities　to　carry　out　multi-level　learning　of　entities　to　improve　the　effect　of　entity　prediction.　Structural　feature　embedding　of　the　graph　takes　advantage　of　the　powerful　feature　aggregation　capability　of　GCN　to　integrate　the　knowledge　graph　as　the　topological　structure　and　node　features　of　graph　data　into　the　representation　learning　of　entities.　Dynamic　embedding　of　entities　takes　advantage　of　the　characteristics　that　entities　will　behave　in　different　states　at　different　times　and　divides　the　entity　features　into　two　parts　for　learning.　One　part　of　the　features　changes　dynamically　over　time　and　the　other　part　retains　its　static　properties.　In　this　paper,　the　probability　model　ConvTransE　is　used　to　construct　the　model　of　relations　and　triples,　ConvTransE　uses　the　convolutional　neural　network　to　extract　the　semantic　information　of　the　triples　and　predict　the　missing　elements.　We　have　conducted　experiments　on　public　datasets　and　achieved　better　results　on　Hit@K　and　MRR.　©　2021　Institute　of　Physics　Publishing.　All　rights　reserved.