Community　detection　is　an　important　tool　for　analyzing　and　understanding　large-scale　complex　networks.　It　can　divide　the　network　nodes　into　multiple　communities,　which　have　dense　intra-community　connections　and　sparse　inter-community　connections.　Traditional　community　detection　algorithms　focus　on　non-attributed　networks　that　contain　only　topological　structures　and　ignore　the　attribute　information　on　the　nodes.　Dual-channel　attribute　network　community　detection　model　optimizes　the　topology　and　attribute　information　as　two　channels,　which　can　make　full　use　of　the　two　types　of　information　and　improve　the　clustering　accuracy　of　the　model.　As　a　classical　mathematical　method　of　community　detection,　non-negative　matrix　factorization　is　only　suitable　for　linear　data,　and　cannot　mine　the　nonlinear　latent　structural　features.　To　address　these　limitations,　this　paper　proposes　a　dual-channel　attributed　graph　community　detection　algorithm　based　on　kernel　matrix　factorization(KDACD).　The　nonlinear　relations　between　nodes　are　learned　by　using　kernel　trick　which　projecting　attribute　features　of　nodes　into　high-dimensional　Hilbert　Spaces,　and　the　robustness　of　the　model　is　improved　by　sparse　constraints　and　manifold　regularization　terms.　Extensive　experiments　on　6　real-world　datasets　verify　the　effectiveness　of　the　algorithm.