Abstract: Wind is intermittent in nature,which makes it difficult to predict wind speed. In order to solve this problems,a deep learning model based on sequence-to-sequence and attention mechanism is proposed to predict ultra-short-term wind speed. Firstly,1-D convolution neural network and Gate Recurrent Unit are used to encode the wind speed sequence data and obtain semantic vectors.Secondly,attention mechanism and Gate Recurrent Unit are used to predict ultra-short-term wind speed. The semantics vectors are decoded dynamically,and the predicted values are output. The model parameters are trained by back propagation algorithm and gradient descent algorithm. The experimental results show that the forecasting model improves the accuracy and robustness of ultra-short-term wind speed prediction. Compared with other models,the proposed model has better generalization ability.