Some　extrapolation　methods　for　Chinese　landscape　painting　based　on　generative　adversarial　network　is　proposed　in　this　paper.　Existing　image　extrapolation　methods　are　mainly　designed　for　natural　images　with　large-scale　regions　containing　same　objective　in　each　one　and　with　standardized　textures,　such　as　grass　and　sky.　They　often　suffer　from　blur　and　boundary　semantic　inconsistency　in　extrapolated　regions　when　they　are　applied　to　Chinese　landscape　painting　that　have　complex　details,　rich　gradations　and　various　strokes.　To　address　those　problems,　a　new　bidirectional　decoding　feature　fusion　network　based　on　generative　adversarial　network　(BDFF-GAN)　is　proposed.　The　generator,　named　UY-Net,　is　designed　with　the　architecture　of　U-Net　and　a　multi-scale　decoder,　which　can　achieve　the　function　of　bidirectional　decoding　features　fusion.　Features　from　different　layers　of　the　encoder　are　assigned　to　corresponding　layers　of　the　multi-scale　decoder,　where　the　first-stage　feature　fusion　is　achieved　by　concatenation　operations　and　therefore　the　connections　between　features　of　different　scales　are　enhanced.　On　the　other　hand,　decoded　features　from　U-Net　part　and　the　multi-scale　decoder　part　at　same　scales　are　fused　by　skipping　connections　to　further　improve　the　performance　of　the　generator.　Benefiting　from　the　subtle　architecture,　UY-Net　can　perform　well　at　semantic　features　and　stroke　transmission　as　well　as　learning.　Moreover,　multi-discriminator　strategy　is　adopted　in　our　method.　A　global　discriminator　takes　the　whole　result　image　as　the　input　to　control　the　global　consistency,　and　a　local　discriminator　takes　the　patch　from　the　junction　of　source　image　part　and　extrapolated　part　as　the　input　to　improve　the　coherence　and　details.　Experimental　results　show　that　BDFF-GAN　performs　well　at　semantic　features　and　textures　learning　with　regards　to　landscape　paintings　and　outperforms　existing　methods　in　terms　of　the　semantic　content　coherence　and　the　naturalness　of　texture　structure　with　regards　to　strokes.　In　addition,　we　provide　an　interface　that　allows　users　to　control　the　outline　of　the　extrapolated　part　by　boundary　guide　lines,　which　achieves　the　controllability　for　the　layout　of　extrapolated　part　and　expands　the　generation　diversity　and　application　interactivity　of　BDFF-GAN.　©　2022,　Science　Press.　All　right　reserved.