Landslide　hazard　assessment　is　critical　for　preventing　and　mitigating　landslide　disasters.　The　tuning　of　hyperparameters　is　of　great　importance　to　achieve　better　accuracy　in　a　landslide　hazard　assessment　model.　In　this　study,　a　novel　approach　is　proposed　for　landslide　hazard　assessment　with　support　vector　machine　(SVM)　as　the　primary　model　and　Bayesian　optimization　(BO)　algorithm　as　the　parameter　tuning　method.　This　study　describes　1711　historical　landslide　disaster　points　in　Nanping　City,　and　a　total　of　12　landslide　conditioning　factors　including　elevation,　slope,　aspect,　curvature,　lithology,　soil　type,　soil　erosion,　rainfall,　river,　land　use,　highway,　and　railway　were　selected.　The　multicollinearity　diagnosis　was　performed　on　the　factors　using　the　Spearman　correlation　coefficient.　For　model　validation,　1711　landslides　and　1711　non-landslides　were　collected　as　the　dataset　and　divided　into　a　training　dataset　(50　%)　and　a　testing　dataset　(50　%).　The　performance　of　the　model　was　evaluated　by　the　confusion　matrix　and　receiver　operating　characteristic　(ROC)　curve.　The　results　of　the　confusion　matrix　accuracy　and　the　area　under　the　ROC　curve　showed　that　the　BO-SVM　model　(89.53　%,　0.97)　performed　better　than　the　SVM　model　(84.91　%,　0.93).　In　addition,　the　landslide　hazard　maps　generated　by　the　BO-SVM　model　had　better　overall　results　than　that　by　the　SVM　model.