Hazard　assessment　of　sudden　geological　disasters　is　of　great　significance　for　disaster　prevention　and　risk　management.　Due　to　different　factors　affecting　the　occurrence　of　disasters　in　different　regions,　it　is　difficult　to　select　appropriate　factors　comprehensively　and　objectively　in　an　actual　evaluation　process.　Machine　learning　has　unique　advantages　in　dealing　with　high-dimensional　nonlinear　problems　of　disaster　systems,　but　its　evaluation　performance　is　limited　because　the　model　is　difficult　to　tune.　This　paper　attempted　to　propose　a　two-way　optimization　method　for　landslide　hazard　assessment.　Based　on　a　factor　sensitivity　index　built　for　quantitative　sensitivity　analysis,　combining　importance　analysis,　correlation　analysis,　and　collinearity　analysis,　and　following　the　principle　of　＇guarantee　sensitivity,　retain　importance,　eliminate　correlation,　and　avoid　collinearity＇,　a　four-dimensional　(4D)　feature　screening　method　was　constructed　to　evaluate　the　comprehensive　optimization　of　factors.　In　order　to　overcome　the　problem　that　the　model　is　difficult　to　tune,　the　Differential　Evolution　(DE)　algorithm　was　further　introduced.　Two　machine　learning　models　with　strong　generalization　ability,　i.e.,　Support　Vector　Machine　(SVM)　and　Multi-Layer　Perceptron　(MLP),　were　optimized.　Finally,　we　took　the　landslide　in　Fujian　Province　as　an　example　to　verify　the　proposed　evaluation　method.　We　found　that　the　4D　feature　screening　method　can　more　objectively　and　comprehensively　select　suitable　hazard　assessment　factors,　thereby　reducing　the　data　dimension　and　reducing　information　redundancy　to　improve　the　performance　of　the　assessment　model.　Ten　suitability　assessment　factors　were　finally　used　for　landslide　hazard　assessment　in　Fujian　Province　including　aspect,　variance　coefficient　in　elevation,　land　use　type,　average　annual　rainfall,　surface　cutting　depth,　distance　to　river,　distance　to　road,　engineering　geological　rock　group,　topographic　wetness　index,　and　stream　power　index.　The　DE　algorithm　can　obtain　better　hyperparameters　from　global　search　and　has　a　significant　optimization　effect　on　SVM　and　MLP,　which　is　beneficial　to　improve　the　evaluation　accuracy　of　the　landslide　hazard　of　the　model.　Compared　with　the　unoptimized　models,　the　AUC　values　of　DE-SVM　and　DE-MLP　increased　by　4.43%　and　4.37%,　respectively.　The　results　of　landslide　hazard　assessment　based　on　two-way　optimization　show　that　rainfall　and　land　use　types　have　an　important　impact　on　the　occurrence　of　landslides　in　Fujian　Province.　Terrain　curvature　elements,　terrain　variability　elements,　and　fault　structures　have　little　impact　on　landslide　occurrence.　The　extremely　high-hazard　areas　generally　have　high　annual　rainfall　and　complex　and　changeful　terrain.　The　extremely　low-hazard　areas　are　mainly　located　along　the　southeast　coast　and　on　both　sides　of　the　Minjiang　River　Basin.　This　research　provides　some　ideas　for　objective　selection　of　influencing　factors　in　landslide　hazard　assessment　and　machine　learning　model　tuning.　©　2022,　Science　Press.　All　right　reserved.