To improve the accuracy of short-term wind direction forecasting, a hybrid model, named EEMD-CNN-GRU, is proposed based on Ensemble Empirical Mode Decomposition (EEMD), Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU).The EEMD is used to decompose the data into multiple components to address the randomness and unsteadiness of wind direction series, then the local connection and weight sharing of CNN are employed to extract the potential features in each component, and the GRU is adopted to reconstruct the extracted features and superpose the predicted values of each component to obtain the final prediction results.The experimental results show that the proposed method outperforms models of BP neural network and long short-term memory (LSTM).