Insufficient training data is often faced in the task of text intent detection, and due to the discreteness of text data, it is difficult to perform data augmentation and improve the performance of the original model with the unchanged label. This study proposes a method combining stepwise data augmentation with a phased training strategy to solve the above problems in the few-shot intent detection. The method progressively augments the original data on whole statements and sample pairs in the same category from both global and local perspectives. During model training, the original data is learned according to different partition stages of the progressive level. Finally, experiments are performed on multiple intent detection datasets to evaluate the validity of the method. The experimental results show that the proposed method can effectively improve the accuracy and the stability of the few-shot intent detection model.