Cross-domain　recommendation　provides　effective　solutions　to　the　problems　of　cold　start　and　data　sparsity　by　transferring　information　from　one　or　more　source　domains　to　another　target　domain.　However,　people＇s　interactive　behaviour　usually　occurs　in　a　sequence,　but　most　cross-domain　recommendation　models　often　ignore　the　sequential　information.　At　the　same　time,　most　sequential　recommendation　models　only　consider　the　case　of　single-domain.　In　this　paper,　we　propose　a　new　cross-domain　sequential　recommendation　model　(SATLR)　based　on　self-attention　and　transfer　learning,　which　make　recommendations　based　on　user　preferences　and　sequential　dependencies　learn　from　cross-domain.　The　experiment　results　demonstrate　that　on　the　three　groups　of　datasets　from　amazon,　our　model　is　better　than　other　recommendation　models.　©　Content　from　this　work　may　be　used　under　the　terms　of　the　Creative　Commons　Attribution　3.0　Licence.