High-accuracy short-term wind power prediction is critical to ensure the power system security. A short-term wind power prediction method based on the information in neighboring wind farms and CNN-BiLSTM is proposed in this paper. In the deep learning prediction modeling process, in addition to using the numerical weather prediction (NWP) of the target wind farm as the input feature, the highly correlated features in neighboring wind farms are also introduced. Firstly, the composite correlation between each neighboring wind farm and the target wind farm in the region is constructed based on the relativity and distance among wind sequences and power sequences, and the highly similar neighboring wind farms are selected as the information source according to the correlation ranking. Then, CEEMDAN frequency domain signal decomposition and time series feature expansion are used to construct a high-dimensional feature set, and floating search feature selection algorithm is introduced to optimize the strongly correlated features. Finally, based on the selected core features, the power prediction model based on CNN-BiLSTM deep learning neural network is built. The results show that the proposed method can effectively improve the prediction accuracy compared with the traditional prediction methods which does not use information from neighboring wind farms.