The invention discloses a wind turbine generator set ultra-short period wind power prediction method based on an improved RBF network. A wind turbine generator set is adopted for the operation of statistic data, parameters closely influencing the output of wind power are reasonably selected, such as the wind speed, wind direction, propeller pitch angle and wind power of the previous period, and a manual neural network-a radial basis function network is utilized to establish a model of corresponding relations between related parameters and the wind power output; and the improved RBF network is utilized to modify the model, whether the node number of a current hidden layer meets a precision requirements is judged, whether the output of nodes of one hidden layer is smaller than one value in a continuous period of learning is judged, the node number of the hidden layer is modified on line in real time, and new learning samples are continuously added along with the development of prediction. The wind power prediction method is high in precision and high in speed.