A shortterm wind power forecasting method based on long shortterm memoryattention (LSTMAttention) network was presented. Firstly, the LSTM network was used to extract the feature information of numerical weather prediction (NWP) data, and the attention mechanism was used to effectively analyze the correlation between input and output of the model, so as to obtain more global features of important moments. Secondly, the convolutional neural network (CNN) was used to extract the local features of NWP data, squeezeexcitation (SE) blocks were introduced to learn the feature weights, and the feature recalibration method was used to improve the network representation ability. Finally, local and global features were fused, and the classification results were output by classifier. A case study of a wind farm in California, American provided by National Oceanic and Atmospheric Administration (NOAA) was conducted to demonstrate the effectiveness of the proposed method. The experimental results showed that LSTMAttention model had higher prediction accuracy than BP neural network, autoregressive integrated moving average (ARIMA) model and LSTM model, which proved the validity of the proposed method.