Abstract: Quick and accurate load prediction is crucial to wind turbine design and safe operation. The loads on the wind turbine are acquired by prototype test and data calibration in this study,and the Pearson coefficient method is then used to analyze the correlation relationships of the load characteristics with the statistical data from experimental turbine and meteorological measurements. The input variables for prediction model are determined based on the correlation ranking. The prediction model for wind turbine loads is established using the extremely random forests algorithm to predict the ultimate load,average load and equivalent fatigue load of turbine at crucial positions. The validated results show that the trained model can predict the load characteristics of the blade,tower top,tower bottom quickly and accurately,and the average determination coefficient R2reaches to 0.96. Therefore,the constructed model can provide effective support for the load monitoring and safe operation of wind turbine.