Diagnosing faults in wind turbine bearings under variable operating conditions is challenging due to signal fluctuations, noise interference, and limited model generalization. This paper proposes an intelligent diagnostic approach that combines noise-resilient feature extraction with optimized neural network learning to capture fault-sensitive features and adapt effectively to varying conditions. Experimental results validate the method’s consistent diagnostic performance, underscoring its practical value and applicability in complex operational environments.