Abstract: To solve the practical problems of wind turbine blade damage diagnosis,we proposed a L-AlexNet method,a type of deep learning algorithm,combined with a machine vision technology.The 8270 wind power generator blades images with a size of 227×227 pixel were captured by a UAV（Unmanned Aerial Vehicle） camera and taken as a training data set.The BP（Back Propagation） neural network,the deep（CNN） Convolutional Neural Network AlexNet,and another deep CNN L-AlexNet classifier were trained accordingly,and the newly added 10 turn of 350 images were used for classification tests.Diagnostic categories include:background,no damage or pseudo damage,repaired,sand holes,cracks,mixed damages.The test results show that the average accuracy rate of LAlexNet classifier is 97.0286%,which is 1.9144% higher than that of the AlexNet classifier,and 26.9622% higher than that of traditional BP network classifier.Therefore,the proposed method,based on the deep learning framework,is effective for the automatic damage diagnosis of wind power generator blade surfaces.