Abstract: Vectorized wiring diagrams for power plants and substations need to be manually drawn and imported by dispatching operation and maintenance personnel with reference to design drawings, which is labor-intensive and error-prone. Aiming at the three core recognition problems, i. e., graphic element, text and wiring relationship of the wiring diagram for power plants and substations, a complete method of wiring diagram recognition for power plants and substations based on artificial intelligence is proposed to significantly improve the efficiency and accuracy of recognition. Through the combination of overlapping sliding window mechanism of hierarchical preprocessing and YOLOv4 algorithm, the problem of “large image and small graphic element detection” of the wiring diagram recognition for power plants and substations is solved. Through the combination of transfer learning and convolutional recurrent neural network, the recognition rate of Chinese electrical texts is improved. Embedded with the electrical domain knowledge and rules, the accuracy of wiring recognition is improved. Finally, according to the obtained electrical component information, text information, connection line information, other downstream tasks are completed. Using the data set of grid wiring diagram derived from the actual dispatching system, the comparison experiments of graphic element, text and wiring recognition are designed to verify the effectiveness of the proposed method.