The　causes　of　landslide　disasters　are　complex,　and　landslide　susceptibility　assessment　is　of　great　significance　for　disaster　warning,　prevention,　and　control　management.　In　the　previous　mapping　studies　on　landslide　susceptibility　assessment,　land　use　change　factor　was　not　considered.　This　paper　proposed　a　combination　of　factors　for　landslide　susceptibility　assessment　by　considering　land　use　dynamic　change　factor.　The　landslide　frequency　ratio　was　used　to　quantitatively　measure　the　correlation　between　land　use　change　and　landslide　development.　And　Logistic　Regression　(LR)　model　was　used　to　compare　the　prediction　ability　of　the　model　before　and　after　the　introduction　of　land　use　change　factor.　We　constructed　three　machine　learning　models:　Decision　Tree　(DT),　Gradient　Boosting　Decision　Tree　(GBDT),　and　Random　Forest　(RF).　We　used　AUC　and　other　indicators　to　compare　model　performance.　Finally,　we　took　Sanming　City　of　Fujian　Province　as　the　study　area　and　the　whole　Fujian　Province　as　the　verification　area　to　conduct　the　landslide　susceptibility　assessment　research.　The　results　show　that　there　is　a　strong　correlation　between　land　use　change　factor　and　landslide　development.　The　inclusion　of　land　use　change　factor　improves　model　prediction　accuracy,　which　indicates　that　it　is　necessary　to　introduce　dynamic　factor　in　the　assessment　of　landslide　susceptibility.　The　verification　results　show　that　RF　model　has　higher　prediction　accuracy　than　DT　and　GBDT.　The　high　landslide　prone　areas　are　mainly　distributed　in　the　west　and　central　of　Sanming　City,　where　the　land　use　change　degree　is　high,　and　the　impact　of　human　activities　is　great.　The　low　landslide　prone　areas　basically　locate　in　the　high-altitude　areas　with　little　influence　of　human　activities.　This　study　provides　a　new　research　perspective　for　landslide　susceptibility　assessment　and　helps　to　explore　the　impact　of　human　activities　on　disaster　formation.　©　2023　Journal　of　Geo-information　Science.　All　rights　reserved.