Community　detection　is　an　important　tool　to　analyze　and　understand　large-scale　graph　networks.　Traditional　non-negative　matrix　factorization　method　use　the　complete　adjacency　matrix,　in　which　the　redundant　information　may　interfere　with　the　learning　of　node　features　and　bring　high　computational　complexity.　Thus,　we　propose　the　Node2vec　enhanced　attributed　graph　matrix　factorization　algorithm(N2V-AGMF)　which　combining　the　graph　embedding　and　non-negative　matrix　factorization.　The　algorithm　uses　the　Node2vec　to　extract　node　low-dimensional　features　based　on　topological　information,　then　combines　them　with　the　attribute　matrix　for　joint　matrix　factorization.　The　low　dimensional　embedding　of　the　adjacency　matrix　enriches　the　representation　of　node　features,　and　effectively　reduces　the　high　computational　complexity　caused　by　the　factorization　of　the　high　dimensional　matrix.　Experiments　were　carried　out　on　5　real　world　data　sets　to　verify　the　effectiveness　of　the　algorithm.　©　2023　SPIE.