In few-shot image classification， the number of samples is usually small. Moreover， existing metric learning models cannot well balance local and global information. They are often affected by complex backgrounds and thus cannot focus well on the main subject information. To address these issues， a few-shot classification model named dual-granularity region confusion networks （DGRCN） that integrates region-reorganized images and sub-block features is proposed. The DGRCN model introduces the multi-granularity idea and proposes the region confusion granulation （RCG） method. Through this method， multi-granularity region-reorganized images and multi-granularity sub-block sequences are obtained， which alleviates the problem of insufficient samples. Secondly， picture-level and patch-level features are extracted from the region-reorganized images and sub-block sequences， respectively. An attention mechanism is added to refine the areas of focus and perform multi-granularity feature fusion based on these two types of features. Finally， the classification results of the dual features are adaptively fused， taking into account both global and local key information. DGRCN is experimented on the public datasets miniImageNet， CUB and Stanford Dogs. Compared with the baseline model DN4， the accuracy has been slightly improved. Experimental results show that DGRCN can effectively improve the performance of few-shot classification tasks.