Community　detection　is　widely　used　in　network　analysis,　which　seeks　to　divide　network　nodes　into　distinct　communities　based　on　the　topology　structure　and　attribute　information　of　the　network.　Due　to　its　interpretability,　nonnegative　matrix　factorization　becomes　an　essential　method　for　community　detection.　However,　it　decomposes　the　adjacency　matrix　and　attribute　matrix　separately,　which　do　not　tightly　incorporate　topology　and　attributes.　And　in　the　problem　of　division　inconsistency　based　on　topology　and　attributes　caused　by　the　mismatch　between　the　topology　similarity　and　attribute　similarity　of　paired　nodes,　it　ignores　the　difference　in　the　matching　degree　of　each　attribute　and　each　node.　In　this　paper,　we　propose　a　nonnegative　matrix　factorization　algorithm　for　community　detection　(MTACD)　based　on　the　matching　degree　between　topology　and　attribute.　First,　we　employ　an　attribute　embedding　mechanism　to　enhance　the　node-attribute　relationship.　Second,　we　design　an　attribute　matching　degree　and　a　node　topology-and-attribute　matching　degree　in　order　to　resolve　the　mismatch　between　topology　and　attribute　similarity.　Experiments　on　both　real-world　and　synthetic　networks　demonstrate　the　effectiveness　of　our　algorithm.　©　2024,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.