Industrial applications persistently face challenges of data scarcity and high annotation costs, for which few-shot object detection (FSOD) provides an innovative solution. To address the limitations of conventional direct matching methods escalated computational complexity and fine-grained feature loss during aggregation, we propose a novel meta-learning framework with an advanced feature aggregation scheme. Specifically, we first develop a Hierarchical Attention Feature Aggregation (HAFA) module that integrates multi-head attention with a Mixture-of-Experts (MoE) mechanism to achieve precise extraction and decoupled representation of fine-grained features. Building upon this foundation, the Multi-Scale Dynamic Fusion (MSDF) module employs a parallelized architecture incorporating diverse feature interaction operators, coupled with attention-based adaptive dynamic weighting to significantly enhance cross-scale detail feature capture. Comprehensive benchmark evaluations on the PASCAL VOC and MS COCO datasets indicate that our approach achieves state-of-the-art performance across most cases.