To address the issues of sparse training data and the inadequacy of a single distance metric in measuring relationships among samples comprehensively in few-shot learning， a few-shot image classification method based on Granular-Ball Prototypical Network （GBProtoNet） was proposed. Firstly， the Ball k-means algorithm was applied to the query set， and category information was obtained by adaptively updating iteratively， after the above， this information was combined with ProtoNet to construct granular-ball prototypes with information from both the query set and the support set， thereby mitigating the problem of limited training data. Secondly， after GBProtoNet feature extraction， a feature selection module was designed to extract important information from samples， and Ball k-means algorithm was used to obtain the cluster centers for categories in the query set， which were then weighted and fused with the original prototypes to construct more representative granular-ball prototypes. Thirdly， the Euclidean distance and cosine distance between the original query set samples and the granular-ball prototypes were computed and multiplied to achieve a comprehensive distance， thereby making distance metric between samples more comprehensive. Finally， according to the nearest neighbor assignment principle， the query set samples were assigned to their categories. Experimental results in 5-way 1-shot and 5-way 5-shot image classification tasks using MiniImageNet and TieredImageNet datasets show that the proposed method improves the classification accuracy by 6.18% and 3.85% on MiniImageNet dataset， and by 6.89% and 3.57% on TieredImageNet dataset， compared to the baseline ProtoNet. Additionally， the time cost required of the proposed method for 5-shot image classification tasks on MiniImageNet dataset is reduced by 72.6% compared to SSL-ProtoNet （Self-Supervised Learning ProtoNet）. These results demonstrate that the proposed method enhances classification accuracy for few-shot learning effectively and has high efficiency.