Abstract: Provided are a wind turbine fault diagnosis method and system based on SVD-SSA-LSTM. The method includes: setting a plurality of monitoring points according to equipment parameters of a wind turbine tower; acquiring vibration signals of each monitoring point according to preset monitoring time nodes, and generating a fault feature data packet according to all the vibration signals; and establishing a fault diagnosis model, and generating a fault diagnosis result according to the fault diagnosis model and the fault feature data packet. The collected vibration signals are decomposed by using singular value decomposition (SVD) noise reduction to remove redundant and noise components therein, then faults of the wind turbine is diagnosed by using a SSA-LSTM fault diagnosis model, and through the capability of a LSTM network to process time-series data, the accuracy of fault diagnosis is improved, while the diagnosis cycle is shorten and the early warning efficiency is improved.