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In recent years, wind power has developed rapidly in China, but the instability and intermittence of wind speed make the wind power have the same nature. Such power injection will bring instability to the operation of the power system. Therefore, prediction of wind power is of great significance to wind power network and utilization. In view of this, short-term prediction of wind power is focused based on LS-SVM, and thus the deterministic short-term prediction is achieved. In addition, the non-parametric statistical method is used to fit the prediction error of the deterministic prediction model to estimate its density function, and calculate the confidence interval of each power segment to obtain probabilistic prediction results. As a result, the practicability and reliability of wind power prediction results are improved. Compared with the commonly used autoregressive moving average model and BP neural network model, the performance and advantages of the method are proved.