Abstract: The performance quality of power grid material suppliers is the basis for the safe and stable operation of power grid,which involves many links and complex risk factors,causing the current research on it is relatively scarce and stays at the level of theoretical analysis.In order to solve this problem,a supplier performance risk prediction model based on XGBoost is proposed,which fully considers various risk factors during the whole process,integrates internal supply chain operation,knowledge map data,external eye inspection,epidemic situation and other data,constructs 191 risk features based on feature engineering for initial training,and retrains 49 selected features after model optimization,taking into account the requirements of prediction accuracy and feature interpretability in actual business,and uses SHAP method to explain the model.Experimental results show that,compared with other three mainstream machine learning algorithms,the accuracy rate,precision rate and KS value are as high as 93.05%,94.45% and 45.38%,which further verifies the feasibility and superiority of XGBoost model in the performance risk prediction.The prediction model can be applied to the power grid supply chain business to further guide the practical application.

Key words: XGBoost, Feature engineering, Supplier performance, Risk prediction, Power grid