Traditional named entity recognition methods can achieve favorable results owing to sufficient supervision data. As far as named entity recognition from electric power texts is concerned, however, the dependence on professional knowledge often makes it difficult to obtain sufficient supervision data, which is also known as a few-shot scenario. In addition, electric power named entity recognition is more challenging than general open domain tasks due to the accuracy requirements of the electric power industry and the more categories of entities in this industry. To overcome these challenges, this study proposes a named entity recognition method based on topic prompts. This method regards each entity category as a topic and uses the topic model to obtain topic words related to the category from the training corpus. Then, it fills in the template and constructs prompt sentences by enumerating entity spans, entity categories, and topic terms. Finally, the generative pre-trained language model is used to rank the prompt sentences and ultimately identify the entity and the corresponding category label. The experimental results show that on the dataset of Chinese electric power named entities to be recognized, the proposed method achieves better results than those offered by several traditional named entity recognition methods.