In order to achieve accurate monitoring of abnormal wind turbine blades, a method combining complementary ensemble empirical mode decomposition with the sound features of wind turbine blades was proposed. Firstly, the voiceprint data of four kinds of fan blades under abnormal working conditions and normal operating conditions are collected and pre-processed for noise reduction, frame division and window addition. Through experimental comparison, the complementary ensemble empirical mode decomposition algorithm is selected for secondary noise reduction of voiceprint data. Secondly, the modal decomposition of frame signals after secondary noise reduction is carried out to extract modal components. The effective modal components were selected by calculating the Pearson correlation coefficient of the modal components, and the characteristics of mel frequency cepstrum coefficient, linear prediction cepstrum coefficient, gammatone cepstrum coefficient, short-time energy and short-time mean zero crossing rate were extracted for each layer of modal components. Finally, based on these feature combinations, support vector machine, naive Bayes and neural network are used as fault classification models to identify voicing data. The research results show that the neural network model based on the combination of the above five vowels features and the parameter optimization can achieve the accurate recognition of blade anomalies, with the recognition accuracy of 97.5%. The model has a good recognition effect on early abnormal fan blades, and has good generalization performance.