With　the　development　of　Internet　finance,　the　importance　of　loan　risk　control　is　increasingly　manifested.　Risk　control　is　the　core　part　of　traditional　financial　industry　and　Internet　finance.　After　investigating　the　latest　developments　in　credit　risk　control　algorithms,　an　improved　stacking　integrated　learning　algorithm　is　proposed.　By　improving　the　feature　selection　steps,　and　using　5　different　learners　for　stacking　integration,　the　performance　of　the　model　is　improved.　The　basic　learners　used　include:　Logistic　Regression,　Random　Forest,　GBDT,　XGBoost,　LightGBM,　among　which　there　are　both　strong　learners　and　weak　learners.　Compared　with　traditional　integrated　learning　methods,　the　accuracy　of　strong　learners　can　be　fully　utilized,　and　use　weak　learners　to　reduce　overfitting.　Finally,　the　accuracy　and　generalization　performance　of　the　model　are　improved.　©　2021　IEEE.