In the field of fine-grained vehicle recognition, deep learning faces a challenge: various new car models are constantly being introduced, but my ability to collect and annotate data is limited, which can lead to the problem of "small sample class incremental learning". In response to the above challenges, this article proposes a new method based on prompt based small sample class incremental learning, aiming to enable the model to recognize existing categories and learn new categories with a small number of new vehicle category samples, without the need for retraining or relying on a large amount of raw data. This method combines the advantages of prompt mechanisms and pre trained visual transformer (ViT) models. We have designed two types of prompts-domain prompts and FSCIL prompts-to address the challenges in FSCIL. In class incremental learning, the average accuracy of Stanford Cars and CompCars datasets reached 70.47% and 73.56%, respectively, which is superior to current existing methods.