Fine-grained bird recognition tasks frequently face the challenges of small interclass and large intraclass differences. In this study, we propose an incremental learning method for fine-grained bird recognition based on prompt learning. Learnable visual prompts are first introduced into the incremental learning model to alleviate the phenomenon of catastrophic forgetting in the incremental learning model. For fine-grained bird recognition, text information of different granularities is introduced as the text prompts in the incremental learning model, which are then fused with the visual prompts to learn the characteristics of different birds from coarse to fine and to improve fine-grained bird recognition accuracy. Numerical experiments on the CUB-200-2011 dataset show that the proposed model has better image recognition accuracy than other incremental learning models. For general image recognition tasks, proposed method exhibits higher recognition accuracy and better anti-forgetting on CIFAR-100 and 5-datasets.