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A few-shot object detection model based on feature reweighting is proposed for remote sensing images with variable scale fuzzy target and complex background. The model consists of three parts: meta feature extractor, feature reweighting extractor and prediction module. The meta feature extractor is composed of CSPDarknet-53, Feature Pyramid Network and Path Aggregation Network, which is responsible for extracting meta features of data. The feature reweighting extractor is used to generate feature reweighting vectors, which are used to adjust meta features to enhance features that are helpful for detecting new classes. The prediction module is composed of the prediction module of YOLOv3. On this basis, the positioning loss function is replaced by CIOU to improve the positioning accuracy of the model. Finally, training and testing are carried out on the NWPU VH R-10 remote sensing data set. Finally, training and testing are carried out on the NWPU VHR-10 remote sensing data set. The experimental results show that compared with the baseline method FSODM, the improves by 19%, 11% and 8% respectively at 3-shot, 5-shot and 10-shot conditions.