At　present,　the　number　of　diabetes　mellitus　patients　in　China　ranks　first　in　the　world,　and　diabetic　kidney　disease　is　the　most　common　disease　in　complications.　Therefore,　it　is　necessary　to　establish　a　predictive　model　for　early　diagnosis　of　diabetic　kidney　disease.　The　model　predicts　the　risk　of　diabetic　kidney　disease　in　the　general　Asian　population,　and　recognizes　high-risk　groups,　then　warns　the　onset　of　diabetes.　The　data　were　obtained　from　the　electronic　medical　record　of　patients　in　Beijing　Pinggu　Hospital.　Twenty-nine　initial　candidate　indicators　including　age,　ALB,　and　A/C　were　selected.　The　random　forest　algorithm　was　used　to　predict　diabetic　kidney　disease,　and　the　classification　accuracy　was　89.831%.　The　importance　weight　ratio　of　each　factor　index　was　also　given,　Microalbuminuria　(ALB),　albumin-to-creatinine　ratio　(A/C),　serum　creatinine　(SCr),　Serum　albumin　(umALB),　and　blood　urea　nitrogen　(BUN)　accounted　for　a　relatively　high　proportion　of　the　weight　of　the　characteristic　variables.　So　these　five　indicators　can　be　the　primary　indicators　of　our　classification　prediction,　and　the　accuracy　can　reach　87.453%.　Some　other　typical　classification　algorithms,　liking　KNN,　logistic　regression,　and　decision　tree,　were　compared　to　classify　and　predict　diabetic　kidney　disease,　and　precision　recall　f1-score　and　area　AUC　under　ROC　curve　were　used　to　evaluate　these　models.　By　experiments,　random　forest　model　was　better　than　other　algorithm　models　on　both　the　classification　accuracy　and　the　evaluation　indicators.　The　results　can　be　applied　to　the　screening　of　patients　with　high　risk　of　diabetic　kidney　disease　and　the　guidance　of　risk　intervention　measures.　Consequently,　the　detection　rate　of　undiagnosed　diabetic　kidney　disease　in　the　population　can　be　improved,　and　the　prevention　effect　of　diabetic　kidney　disease　can　be　enhanced　as　well.　©　2020　ACM.