The existing two-stage few-shot object detection methods based on fine-tuning are not sensitive to the features of new classes， which will cause misjudgment of new classes into base classes with high similarity to them， thus affecting the detection performance of the model. To address the above issue， a few-shot object detection algorithm that incorporates multi-scale and attention mechanism was proposed， namely MA-FSOD （Few-Shot Object Detection via fusing Multi-scale and Attention mechanism）. Firstly， grouped convolutions and large convolution kernels were used to extract more class-discriminative features in the backbone network， and Convolutional Block Attention Module （CBAM） was added to achieve adaptive feature augmentation. Then， a modified pyramid network was used to achieve multi-scale feature fusion， which enables Region Proposal Network （RPN） to accurately find Regions of Interest （RoI） and provide more abundant high-quality positive samples from multiple scales to the classification head. Finally， the cosine classification head was used for classification in the fine-tuning stage to reduce the intra-class variance. Compared with the Few-Shot object detection via Contrastive proposal Encoding （FSCE） algorithm on PASCAL-VOC 2007/2012 dataset， the MA-FSOD algorithm improved AP₅₀ for new classes by 5.6 percentage points； and on the more challenging MSCOCO dataset， compared with Meta-Faster-RCNN， the APs corresponding to 10-shot and 30-shot were improved by 0.1 percentage points and 1.6 percentage points， respectively. Experimental results show that MA-FSOD can more effectively alleviate the misclassification problem and achieve higher accuracy in few-shot object detection than some mainstream few-shot object detection algorithms.