Abstract: To tackle the issue of the low classification efficiency of defect echo signals of wind turbine blades,a particle swarm optimization and extreme gradient boosting algorithm（PSO-XGBoost）is proposed in this paper. Primarily,feature extraction of the defect echo data is applied via variation mode decomposition（VMD）combining with fuzzy entropy to establish XGBoost multiclassification model. Subsequently,XGBoost hyperparameters are optimized via PSO algorithm to establish a PSO-XGBoost multiclassification model.The algorithm of combining of PSO with XGBoost improves the prediction accuracy of wind power blade defects,and reduces the error of defect classification. The simulation results show that PSO-XGBoost algorithm has prediction accuracy by compared with XGBoost and other algorithms,the defects classification accuracy rate of PSO-XGBoost algorithm is up to 98%. Consequently,the PSO-XGBoost algorithm effectively enhances the accuracy of wind turbine blade defect classification.