In the process of building a personal credit risk evaluation model, feature engineering largely determines the performance of the evaluator. Traditional feature selection methods cannot fully consider the impact of high-dimensional indicators on the evaluation results, and most studies artificially determines the size of the feature set in the process of building the model, leading to high randomness and low credibility. Therefore, a random forest model (IV-XGBoostRF) based on traditional risk control indicators to optimize XGBoost is proposed. The traditional risk control indicators IV and XGBoost are combined to screen the original feature set to build a relatively complete credit evaluation model. The results of comparison experiments show that the accuracy of the improved random forest model is increased by 0.90%, and other evaluation indicators are better than the traditional credit evaluation model, which proves the feasibility of the feature selection method and has certain application value.