Histopathological image analysis is a critical task, as it provides the gold standard for clinical cancer diagnosis. Traditional histopathology image analysis relies on extensive datasets with annotations for training and testing purposes. However, the digitisation and labelling of histopathological images is a time-consuming and labour-intensive process, which limits the amount of annotated data. To alleviate this problem, we propose a novel Text-Guided Multi-Task Few-shot Learning framework called TGMT-FSL. Specifically, we first extract image and text feature information separately, then construct a feature map between the two, and finally construct an image-level similarity space and use multiple loss functions to train the model parameters. Extensive experiments evaluate the effectiveness of our methods on EBHI dataset, which fully demonstrate the potential of clinical application of the method in the early diagnosis and precision treatment of gastrointestinal cancer.