Given　the　time-consuming　nature　of　compiling　landslide　inventories,　it　is　increasingly　important　to　develop　transferable　landslide　susceptibility　models　that　can　be　applied　to　regions　without　existing　data.　In　this　study,　we　propose　a　feature-based　domain　adaptation　method　to　improve　the　transferability　of　landslide　susceptibility　models,　especially　in　＂no　sample＂　areas.　Two　typical　landslide-prone　areas　in　Fujian　province,　southeastern　China,　were　chosen　as　research　cases　to　test　the　practicality　of　the　transfer　effect.　Five　conventional　machine　learning　algorithms　(Support　vector　machines　(SVM),　Random　Forest　(RF),　Logistic　Regression　(LOG),　K-nearest　neighbor　(KNN),　and　Decision　tree　(C4.5))　are　used　to　model　landslide　susceptibility　in　sampled　areas　(source　domain),　and　a　feature　transfer-based　landslide　susceptibility　evaluation　model　is　constructed　under　coupled　feature　transfer　methods　to　evaluate　the　susceptibility　of　landslide　in　un-sampled　areas　(target　domain).　The　results　showed　that　feature　transfer　can　effectively　improve　the　transferability　of　different　machine　learning　models　for　cross-regional　prediction　(The　indicators　have　improved　overall　by　8.49%),　with　SVM　(increased　by　13.68%)　and　LOG　(increased　by　10.19%)　models　showing　the　most　significant　improvements.　The　feature-based　domain　adaptive　method　can　alleviate　the　burden　of　collecting　and　labeling　new　data,　and　effectively　improve　the　assessment　performance　of　machine　learning-based　landslide　susceptibility　models　in　un-sampled　areas.　This　is　a　new　solution　for　landslide　susceptibility　assessment　in　completely　＂no　sample＂　areas.　(c)　2024　International　Association　for　Gondwana　Research.　Published　by　Elsevier　B.V.　All　rights　reserved.