In order to solve the problem of the wind turbine blade surface defect defection which has low detection recognition rate and can be easily affected by the light, this paper puts forward a wind turbine blade surface defect detection method based on convolutional neural network which combines local binary patterns with core extreme learning machine. The convolutional neural network introducing attention mechanism is used to extract deep information of images. Then, local binary patterns characteristics which can describe shallow texture information of images are also exacted. Besides, the principal component analysis can reduce local binary patterns characteristic dimension. Serial combination is then done to these two complementary characteristics which can describe images from different levels and the improved sparrow search algorithm is used to optimize Kernel extreme learning machine parameters. Besides, the syncretic feature training model is used to obtain the optimal model for defect recognition. The experiment shows that the classification accuracy rate after the training of self-built data sets can reach 97.5% and that the kappa coefficient can reach 95.1. Compared with single feature detection, the classification accuracy is significantly improved. The actual verification of the wind farm shows that the average classification accuracy of the model is 96.3%, the kappa coefficient is 94.5, and the missing rate is significantly reduced.