Few-shot object detection is a valuable task and is yet to achieve great progress. Most research studies generate class prototypes by support samples to guide the detection. However, there still exists some challenges with this type of approach. On the one hand, the performance is highly sensitive to the quality of class prototype encoding. But limited training data impede the procession of class prototype learning. On the other hand, similar class prototypes distribute densely in the feature space, which easily leads to misjudgment. In this paper, we propose a better class feature representation (BFR) method to obtain more representative and more discriminative class prototypes with a small size of samples. First, BFR obtains the support set feature vector with richer semantic information via different feature aggregation methods. In addition, the distance metric function is used to identify the outliers and reduce the noisy interference with dynamic weights adjustment. Finally, BFR constrains the distribution of feature prototypes through distance loss, so that different prototypes are far away from each other in the feature space to reduce misjudgment. Extensive experiments on public benchmark datasets show that our method achieves superior performance with the main components bringing an 11.3% performance improvement.