It　is　crucial　to　create　a　＇migratable＇　landslide　susceptibility　model　considering　the　time-consuming　process　of　recording　landslides.　However,　a　sample　set　of　all　known　landslide　data　areas　must　be　used　in　forecasting　the　susceptibility　of　unsampled　regions　adequately.　In　this　paper,　we　attempt　to　develop　a　TCA-CNN　model　to　enable　trans-regional　landslide　susceptibility　evaluation,　which　is　based　on　the　adaption　domain　of　transfer　learning　by　integrating　the　transfer　component　analysis　(TCA)　with　deep　learning　convolutional　neural　network　(CNN).　The　spatial　database　of　landslides　is　constructed　by　extracting　11　environmental　factors　of　the　reservoir　bank　area,　then　the　Mianhuatan　reservoir　area　without　samples　is　then　predicted　using　the　susceptibility　model　from　the　Chitan　reservoir　area　with　samples.　The　results　show　that:　(1)　The　maximum　mean　discrepancy　(MMD)　of　data　from　different　study　areas　treated　by　TCA　decreased　significantly　(0.022),　and　the　data　is　approximately　identically　distributed;　(2)　The　trans-regional　prediction　accuracy　by　the　TCA-CNN　model　is　0.854,　which　is　higher　than　that　of　the　CNN　model　(0.791).　And　it　can　be　verified　that　the　proportion　of　landslide　frequency　falling　into　the　high/extremely-high　susceptibility　interval　is　the　highest　(89.1%)　in　the　historical　landslide;　(3)　The　area　under　the　receiver　operating　characteristic　(ROC)　curve　of　the　TCA-CNN　model　is　0.93,　which　is　higher　than　that　of　the　CNN　model　(0.90).　It’s　obvious　that　the　TCA-CNN　model　can　effectively　use　the　samples　data　of　the　modeling　area　to　realize　the　susceptibility　evaluation　of　the　unsampled　area.　Compared　with　the　traditional　machine　model,　TCA-CNN　model　has　higher　and　more　stable　prediction　accuracy　and　stronger　generalization　ability　in　cross-region　prediction.　©　2024　China　University　of　Geosciences.　All　rights　reserved.