The invention discloses a large wind turbine variable pitch system identification method based on an optimized RBF neural network. The method comprises the following steps that firstly, dynamic optimization improvement is carried out on a network structure by adopting an output sensitivity method on the basis of the traditional neural network identification algorithm technology, simulation software is adopted to control simulation to obtain experimental data by adopting a Bladed wind turbine from a great Britain company named Grarrad Hassan Partners, the wind speed v and the pitch angle beta are used as input signals, and the power generation power P serves as an output signal. Further, according to the system identification principle, a model and related measurement information are used for building an identification system framework. Secondly, the RBF is used for identifying the algorithm due to the strong nonlinear mapping capability of the neural network, under the excitation of asystem input signal, the identification system infinitely and approximately outputs the actual power output of the system. Finally, the problem that the network learning speed rate is difficult to select is solved, a gradient descent method and an optimization algorithm are provided, and the optimal learning speed rate of the network structure is derived. The method has high self-adaptive capacityand anti-interference capability, and has a certain practical value.