Deep learning-based object detection algorithms have matured considerably. However, detecting novel classes based on a limited number of samples remains challenging as deep learning can easily lead to feature space degradation under few-shot conditions. Most of the existing methods employ a holistic fine-tuning paradigm to pretrain on base classes with abundant samples and subsequently construct feature spaces for the novel classes. However, the novel class implicitly constructs a feature space based on multiple base classes, and its structure is relatively dispersed, thereby leading to poor separability between the base class and the novel class. This study proposes the method of associating a novel class with a similar base class and then discriminating each class for few-shot object detection. By introducing dynamic region of interest headers, the model improves the utilization of training samples and explicitly constructs a feature space for new classes based on the semantic similarity between the two. Furthermore, by decoupling the classification branches of the base and new classes, integrating channel attention modules, and implementing boundary loss functions, we substantially improve the separability between the classes. Experimental results on the standard PASCAL VOC dataset reveal that our method surpasses the nAP50 mean scores of TFA, MPSR, and DiGeo by 10.2, 5.4, and 7.8, respectively.