Landslide　susceptibility　mapping　is　crucial　in　mitigating　the　risk　of　regional　landslide　hazards.　The　main　objectives　of　this　study　are　to　design　and　analyze　the　impact　of　three　non-landslide　data　generation　methods　on　landslide　susceptibility　mapping.　The　specific　non-landslide　data　selection　methods　are　the　random　generation　of　non-landslide　points　through　different　distance　landslide　buffers,　by　selecting　areas　with　low　landslide　influence　in　the　landslide　condition　factor　layer,　and　based　on　the　results　of　the　information　value　model　partitioning　by　selecting　low　to　medium　susceptibility　zones.　There　were　14　landslide　condition　factors　used　for　the　landslide　susceptibility　mapping,　and　correlations　between　landslides　and　condition　factors　were　analyzed　using　the　Gini　index.　The　70%　dataset　was　modelled　using　a　logistic　regression　model　as　well　as　an　artificial　neural　network　model.　Finally,　statistical　metrics　and　AUC　values　were　used　for　the　30%　validation　data　for　comparing　the　predictive　performances　of　different　non-landslide　data　generation　strategies　in　the　model.　Based　on　the　validation　results,　models　A6　and　B6　performed　the　best,　with　respective　AUC　values　of　0.997　and　0.995.　The　findings　show　that　the　non-landslide　samples　generated　using　the　low　susceptibility　interval　of　the　information　value　model　have　the　best　performance.