Provided is a method of forecasting the wind speed of a wind power station. According to the method, firstly, attribute reduction is conducted on various factors affecting the wind speed of the wind power station in a fuzzy rough set method, redundant information is removed, and input variables of neural network forecasting models are obtained; then, by means of the clustering method of conducting an improvement through the weighting Euclidean distance, data having high similarity are extracted to serve as training samples of the neural network forecasting models, and various forecasting models are trained by using the clustered data; finally, the matched forecasting model is selected for forecasting the wind speed according to a current attribute value. On the basis of traditional neural network forecasting models, the method optimizes the input variables and the training samples, affecting forecasting performance of neural networks, of the models, and the generalization ability of the models is enhanced greatly. Test results show that the method can greatly enhance the forecasting performance of the neural networks and effectively enhance forecasting accuracy of the wind speed of the wind power station.