In　order　to　obtain　the　aerodynamic　loads　of　the　vibrating　blades　efficiently,　the　eXterme　Gradient　Boosting　(XGBoost)　algorithm　in　machine　learning　was　adopted　to　establish　a　three-dimensional　unsteady　aerodynamic　force　reduction　model.　First,　the　database　for　the　unsteady　aerodynamic　response　during　the　blade　vibration　was　acquired　through　the　numerical　simulation　of　flow　field.　Then　the　obtained　data　set　was　trained　by　the　XGBoost　algorithm　to　set　up　the　intelligent　model　of　unsteady　aerodynamic　force　for　the　three-dimensional　blade.　Afterwards,　the　aerodynamic　load　could　be　gained　at　any　spatial　location　during　blade　vibration.　To　evaluate　and　verify　the　reliability　of　the　intelligent　model　for　the　blade　aerodynamic　load,　the　prediction　results　of　the　machine　learning　model　were　compared　with　the　results　of　Computation　Fluid　Dynamics　(CFD).　The　determination　coefficient　R-2　and　the　Root　Mean　Square　Error　(RMSE)　were　introduced　as　the　model　evaluation　indicators.　The　results　show　that　the　prediction　results　based　on　the　machine　learning　model　are　in　good　agreement　with　the　CFD　results,　and　the　calculation　efficiency　is　significantly　improved.　The　results　also　indicate　that　the　aerodynamic　intelligent　model　based　on　the　machine　learning　method　is　worthy　of　further　study　in　evaluating　the　blade　vibration　stability.