A fault diagnosis of wind turbine icing characteristics based on LeNet5like transfer learning method is proposed, to address the problems of low accuracy and slow modelling speed of icing characteristics fault models, which wind turbine units are in offshore wind farms and high altitude areas. Firstly, the recorded data from the SCADA system and the wind turbine icing situation are pre-processed to build a training dataset; secondly, the icing fault diagnosis model is constructed based on the improved LeNet5like network to extract the correlation feature information between multiple variables in the dataset; then, the model is trained by the transfer learning finetuning to achieve the rapid establishment of ice-cover fault diagnosis models for other wind turbines; finally, the model is experimentally validated to have an icing fault diagnosis accuracy of 98. 90% , a 28 s reduction in training time and an improvement of about 15. 91% over the transfer module-free network, verifying the accuracy and speed of the LeNet5like based transfer learning wind turbine blade icecover fault diagnosis method.