The core task of places such as power sales offices is to recognize the user's intention, and current intention recognition methods require a large amount of data to assist in model training. But for these places, it is very difficult to collect data on a large scale. Therefore, it is very important to utilize the training samples efficiently based on the limited number of samples in the dataset. In summary, this paper proposes a semantic distance-based curriculum learning strategy for the task of electric power intent recognition, which can train and learn the samples more efficiently. The experimental results show that the curriculum learning strategy can significantly improve the recognition accuracy of the business on the task of electricity business hall intention recognition.