Based on the P2P platform, this paper uses the information of the individual borrowers of the P2P platforms to establish a credit risk assessment indicator system to identify borrowers who may default. Therefore, this paper proposes a new LGB-BAG model based on LightGBM (a tree-based Boosting model) and Bagging, which effectively combines the advantages of Boosting and Bagging. The results show that when N increases to a certain extent, the F1 mean of LGB-BAG is higher than that of LightGBM and random forest; and the F1 variance of LGB-BAG is also smaller than that of other two models. The F1 mean of LGB-BAG can reach up to 0.71175. While the sample dataset needs to be expanded, the LGB-BAG model could identify credit risks more effectively.