Creating　a　complete　landscape　painting　via　computer　simulation　is　difficult　without　studying　from　global　layout　viewpoint.　To　address　this　issue,　a　layout-guided　Chinese　landscape　painting　simulation　method　for　a　complete　painting　generation　is　proposed.　The　characteristics　of　landscape　paintings　are　taken　into　account　in　the　design　of　feasible　structures　of　layout　label　maps.　Composition　forms　and　elements　of　landscape　paintings　can　be　depicted　using　those　structures.　On　the　basis　of　condition　generative　adversarial　network　(CGAN)　approach,　a　multi-scale　feature　fusion　CGAN　(MSFF-CGAN)　is　designed　based　on　layouts　and　touches　of　landscape　paintings.　The　proposed　network　is　trained　to　accomplish　heterogeneous　transfer　from　a　layout　label　map　to　a　simulated　landscape　painting.　To　deal　with　rare　availability　of　layout　label　maps　for　network　training,　a　color　pixel　clustering　algorithm　with　semantic　correlation　is　used.　In　order　to　enhance　the　artistic　reality　of　the　generated　landscape　painting,　a　super　resolution　network　named　MemNet　is　incorporated　to　refine　the　texture　details.　Experimental　results　show　that　the　proposed　method　is　superior　to　existing　methods　in　both　integrity　and　artistic　reality.　Moreover,　the　proposed　method　can　be　used　to　handle　simple　graffiti　sketches　and　modify　simulated　landscape　paintings　by　editing　label　maps.　©　2019,　Science　Press.　All　right　reserved.