The main part of drawing the knowledge map of electrical power systems is the extraction of power knowledge. In the traditional supervised-learning-based single neural network models, CNN performs well in extracting the most important local features but is not suitable for processing sequence input, and RNN is strong in tackling serialization tasks but weak in extracting important features. To solve these problems, this study puts forward a model based on GRU and PCNN. Compared with traditional models, this model combining the advantages of the GRU helped model and the PCNN model can obtain impressive results and effectively extract the knowledge of electrical power systems.