In order to solve the problem of randomness and volatility in wind power forecasting, a short-term prediction method of wind power based on improved fuzzy C-mean soft clustering is proposed. In this method. First, the data of historical power and data of numerical weather forecast are clustered respectively. Second, subsets of samples are combined according to the time continuum principle. Finally, multiple neural network prediction models are established according to cassification modeling idea. The data of the subset of samples belonging to the prediction point is taken as the training data, then the power value of the prediction period is predicted. The proposed method is applied to the actual wind power forecasting. The results show that the accuracy of short-term wind power forecasting is improved by using the proposed method.