This　study　employs　LendingClub　data　in　the　field　of　personal　credit　risk　control　as　an　illustrative　case.　Various　data　mining　models,　and　support　vector　machine,　are　utilized　for　training　purposes.　Additionally,　a　Stacking　model　is　integrated　into　the　analysis　to　forecast　customer　default　likelihood.　Subsequently,　lending　decisions　are　made　in　accordance　with　these　predictions.　The　outcomes　indicate　a　reduction　in　customer　default　rates　compared　to　scenarios　without　the　application　of　data　mining　models,　thereby　achieving　our　goal　of　risk　control.