Few-shot object detection aims to achieve object localization and recognition on novel classes with limited training instances. Due to the constraints of the two-stage fine-tuning mechanism, existing models lack the ability of knowledge reasoning. When transferring the base model to novel class detection, we add a region of interest feature transfer branch, which establishes a message transfer mechanism between complex instances, ensuring mutual attraction between instances of the same category while allowing for association across different categories. Specifically, a self-attention message transfer graph is constructed to facilitate the propagation of attribute information among target instances. Second, a box transfer loss function is proposed to combine the semantic relationships among instances to promote mutual exclusion among instances with significant category attribute bias, thereby constructing better category feature representations. Finally, we demonstrate the effectiveness of our proposed framework compared to other state-of-the-art methods on two popular datasets: PASCAL VOC and MS-COCO.