In real scenarios, the application often faces the problems of data scarcity and dynamic data changes. Few-shot incremental learning aims to use a small amount of data to infer data knowledge and reduce the model’s catastrophic forgetting of old knowledge. Existing few-shot incremental learning algorithms (CEC, FACT, etc.) mainly use visual features to adjust the feature encoder or classifier, so as to achieve the model’s transfer to new data and anti-forgetting of old data. However, the visual features of a small amount of data are often difficult to model a complete feature distribution of a class, resulting in weak generalization ability of the above algorithms. Compared with visual features, the text features of image class descriptions have better generalization and anti-forgetting abilities. Therefore, based on the visual language model (VLM), this study investigates the few-shot incremental learning based on textual knowledge embedding and realizes the effective learning of new and old class data in few-shot incremental learning by embedding text features with anti-forgetting ability in visual features. Specifically, in the basic learning stage, the study uses the VLM to extract the pre-trained visual features and class text descriptions of the image. Furthermore, the study uses the text encoder to project the pre-trained visual features to text space. Next, the study uses the visual encoder to fuse the learned text features and pre-trained visual features to abstract visual features with high discrimination ability. In the incremental learning stage, the study proposes the class space-guided anti-forgetting learning and uses the class space encoding of old data and new data features to fine-tune the visual encoder and text encoder, so as to achieve new data knowledge learning while reviewing old knowledge. This study also verifies the effectiveness of the algorithm on four datasets (CIFAR-100, CUB-200, Car-196, and miniImageNet), proving that textual knowledge embedding based on VLM can further improve the robustness of few-shot incremental learning on the basis of visual features.