Name entity recognition is an important part in the power knowledge map construction in power dispatching field. Currently, machine learning and deep learning models are used to name entity recognition in the general field or other professional fields. In order to solve the named entity recognition in the power dispatching field, the Transformer-BiGRU-CRF model is researched. The character vector of the corpus is obtained through the Transformer model, and the named entity recognition is performed through BiGRU-CRF. There are two training methods in the training process, the first method is only to train the parameters of the BiGRU-CRF part; the second method is to train the whole model parameters including the Transformer part. Finally, it is found that the first approach reaches the stationary state in less time, but the accuracy rate is about 5% higher for the second approach.