In recent years, object detection in scenarios with fewer samples has attracted widespread attention. Due to the limited information provided by the few samples, most of few-shot object detection models are studied using the improved Faster RCNN detection framework. However, due to the potential module contradiction problem in the Faster RCNN framework, the feature capture and classification capabilities of the existing few-shot object detection models need to be improved. In order to solve the above problems, this paper adds a gradient decoupling mechanism based on the Faster RCNN framework to alleviate the negative impact of the conflict between RPN and RCNN on the backbone network during the backpropagation process. In order to improve the feature detection ability of the object detection model, this paper adopts meta-learning framework, integrates the distillation module based on attention mechanism and the multi-scale attention module, and makes full use of the information of the query set and support set to capture more global feature information. A large number of experiments have proved that under the setting of randomly sampled shot amount k=1, 2, 3, 5, 10, the improved model can reach 21.8%, 34.7%, 40.9%, 44.5%, 51.7% mAP (AP50) on the new class of Pascal VOC dataset, respectively. Under the k=10, 30 setting, the improved model achieves 25.1% and 27.6% mAP (AP50) on the new class of the COCO dataset, respectively.