Aiming at the problem of low prediction accuracy caused by strong intermittency and high random fluctuation of wind power, this paper combines data decomposition technology, artificial intelligence-based prediction model and error correction technology, and proposes a novel combined method for short-term wind power prediction (EEMD-GRU-MC) which integrates ensemble empirical mode decomposition (EEMD), gated recurrent unit (GRU) and Markov chain (MC). Firstly, EEMD algorithm is used to decompose the historical wind power sequence into a group of relatively stationary sub-sequence to reduce the influence of random fluctuation component and disorder noise on the prediction model. Then, GRU model is employed to predict each sub-sequence, and the predicted values of each sub-sequence are superimposed to get the preliminary prediction results. Finally, in order to further improve the prediction accuracy, MC is used to predict the future state of the residual, and the prediction results of the EEMD-GRU model are further modified. The short-term power prediction of a wind farm in Yunnan Province is taken as an example to verify the proposed method. A large number of numerical examples show that compared with ARIMA, LSTM, GRU, EEMD-LSTM and EEMD-GRU models, the combined prediction method proposed in this paper has stronger prediction accuracy and generalization ability, and the average absolute prediction error in different seasons is less than 2%, showing a good short-term wind power prediction prospect.