In order to fully utilize the key information in support features and query features， a few-shot object detection method based on feature fusion and enhanced attention was proposed， namely FFA-FSOD （Feature Fusion and enhanced Attention Few-Shot Object Detection）. Firstly， the iterative Attention Feature Fusion （iAFF） module was introduced to effectively fuse the key features of the support image and the query image. Secondly， the feature enhancement operation was added after the iAFF module， which made full use of the support feature information to enhance the object features in the query image. To avoid the loss of part of the details of the query image after the above two operations， the Multi-Scale Channel Attention Module （MS-CAM） was improved in the iAFF module to capture more context information. Experimental results on MS COCO dataset under 2-way 10-shot condition show that compared with FSOD （Few-Shot Object Detection） method， after adding the iAFF module， feature enhancement operation and improving MS-CAM， FFA-FSOD has mean Average Precision （mAP） increased by 8.0%. Experimental results show that the proposed feature fusion enhancement method pays full attention to the details of features， thus achieving better detection effect of few-shot objects.