Existing meta-learning based few-shot object detection methods suffer from limitations in learning representative prototypes. Specifically, directly aggregating bounding box contents from support images into prototypes renders these methods vulnerable to background noise and the morphological intricacies of objects. Furthermore, these methods neglect the varied contributions of intra-class image-specific prototypes and fail to leverage semantic information effectively during prototype generation, resulting in suboptimal class representations due to naive average aggregation. To address these issues, we propose a Dual Prototype-Enhancement Network (DPENet), designed to optimize prototypes by improving support feature representation and enhancing prototype discriminability. Specifically, we introduce an Object Enhancement Module (OEM) based on dynamic hypergraph construction. This module employs hypergraph convolution to adaptively capture complex high-order semantic interactions among highly similar regions within support features, thereby highlighting salient features of target regions, suppressing background noise, and enhancing support feature representation. Moreover, we propose a Semantic Fusion Perception Module (SFPM) that generates more discriminative class-specific prototypes by integrating weighted intra-class prototype representations with text-based semantic embeddings. Experimental results demonstrate that DPENet significantly outperforms existing methods on the PASCAL VOC and MS COCO datasets.