In the power system, distribution scheduling is complex and well-coordinated, which mostly depends on the experience and subjective judgment of staff and is prone to mistakes. Therefore, it is urgent to use intelligent means to help analyze and generate maintenance plans. Named entity recognition is a key technology in the construction of the knowledge graph of power distribution networks and the question answering system, which can recognize named entities in unstructured data. In view of the complexity and strong correlation of distribution maintenance data, this study adopts the deep learning model BERT-IDCNN-BiLSM-CRF. Compared with the traditional model BERT-BiLSTM-CRF, this model integrates the neural network model IDCNN, makes better use of the performance of GPU, and improves the efficiency on the premise of ensuring recognition accuracy. The labeled maintenance plan data are trained, and the proposed model is compared with other commonly used models. The results reveal that the proposed model achieves the best effect in terms of the recall rate, accuracy rate, and F1 value, and its F1 value can reach 83.1%. The model has achieved good results in the recognition of distribution network data.