Link　prediction　aims　to　predict　missing　links　or　eliminate　spurious　links　by　employing　known　complex　network　information.　As　an　unsupervised　linear　feature　representation　method,　matrix　factorization　(MF)-based　autoencoder　(AE)　can　project　the　high-dimensional　data　matrix　into　the　low-dimensional　latent　space.　However,　most　of　the　traditional　link　prediction　methods　based　on　MF　or　AE　adopt　shallow　models　and　single　adjacency　matrices,　which　cannot　adequately　learn　and　represent　network　features　and　are　susceptible　to　noise.　In　addition,　because　some　methods　require　the　input　of　symmetric　data　matrix,　they　can　only　be　used　in　undirected　networks.　Therefore,　we　propose　a　deep　manifold　matrix　factorization　autoencoder　model　using　global　connectivity　matrix,　called　DM-MFAE-G.　The　model　utilizes　PageRank　algorithm　to　get　the　global　connectivity　matrix　between　nodes　for　the　complex　network.　DM-MFAE-G　performs　deep　matrix　factorization　on　the　local　adjacency　matrix　and　global　connectivity　matrix,　respectively,　to　obtain　global　and　local　multi-layer　feature　representations,　which　contains　the　rich　structural　information.　In　this　paper,　the　model　is　solved　by　alternating　iterative　optimization　method,　and　the　convergence　of　the　algorithm　is　proved.　Comprehensive　experiments　on　different　real　networks　demonstrate　that　the　global　connectivity　matrix　and　manifold　constraints　introduced　by　DM-MFAE-G　significantly　improve　the　link　prediction　performance　on　directed　and　undirected　networks.