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In order to improve the prediction accuracy of short-term wind power, a prediction method of cuckoo search algorithm (Cuckoo Search Algorithm, CS) to optimize support vector regression (Support Vector Regression, SVR) machine is proposed. The method preprocesses the input data according to the upper cutoff point and the lower cutoff point to eliminate the abnormal data. Then, the wind speed, average wind speed, fan state and other attribute data in the input data were used as the input of the SVR algorithm model, and the wind power data was used as the output of the SVR algorithm model to establish the SVR prediction model of short-term wind power. Considering that the SVR algorithm is difficult to select the optimal parameters, a CS-SVR prediction model for short-term wind power is established by using the cuckoo algorithm optimize the SVR parameters. Compared with SVR and PSO-SVR prediction models, the simulation experiment was conducted. Experimental results show that the CS-SVR prediction model has higher prediction accuracy.