Aiming at the problem of insufficient wind turbine gearbox fault feature extraction and low fault diagnosis rate, a wind turbine gearbox fault diagnosis method based on RF feature optimization combined with WOA-ELM feature identification is proposed. Firstly, the wind turbine gearbox time domain, frequency domain, and time-frequency domain features are extracted to construct a multi-domain high-dimensional feature set. Secondly, the RF is used to rank the feature importance and extract 10-dimensional preferred features. Finally, the input weights and implied layer thresholds of the ELM model are optimally adjusted using WOA to achieve wind turbine gearbox fault classification and identification. The experimental results show that the average diagnosis rate of this method can reach 99.81%, and the diagnosis accuracy is higher than that of the comparison methods and the diagnosis time is the least, which can effectively diagnose the wind power gear box faults.