The invention provides an Internet financial risk control model based on XGBoost. The Internet financial risk control model based on XGBoost comprises the following steps: S1, extracting an appropriate modeling sample client; s2, obtaining online loan data of a sample client, and extracting a feature variable corresponding to the online loan data; s3, defining 'good ' and 'bad' of the modeling sample according to the repayment behavior of the customer, the quality of the target customer group and the product type; s4, data processing, including dirty data cleaning, missing value processing andabnormal value processing; s5, feature engineering including feature construction and feature screening; s6, data set division: randomly or cross-time dividing a training set and a verification set;s7, performing training by applying an XGBoost algorithm, and performing model parameter adjustment; and S8, evaluating the model: evaluating the quality of the model according to the evaluation indexes. On one hand, third-party data are additionally used, the dimensionality of risk identification is increased, and meanwhile, the efficiency and robustness of a model algorithm are optimized throughan XGBoost algorithm with high prediction capability; on the other hand, the accuracy of the model is continuously optimized through XGBoost algorithm parameter adjustment and model evaluation, and the method is more suitable for the demand of big data risk control.