Deep metric learning has been widely used in many visual tasks. Its key idea is to increase the similarity of positive samples and decrease the similarity of negative samples through network training. To achieve this purpose, many studies excessively extend the distance between the query sample and hard negative samples. This may compress the distance between similar samples of other classes, causing these samples to cluster together. We call this phenomenon Negative Sample Aggregation. To address this problem, first, we propose a weighting method based on the Ranking Similarity of sample pairs, short for RS. The proposed weighting method can not only enlarge the distance between the query sample and hard negative samples, but also maintain the embedding distribution of proximal negative samples. Second, we propose a Top-nk sampling method, which can dynamically adjust the sampling strategy according to the distribution of a dataset. It solves the problem that the descent direction of the network gradient is inconsistent with the optimization target. The effectiveness of our methods is evaluated by extensive experiments on four public datasets and compared with that of other state-of-the-art methods. The results show that the proposed method obtains excellent performance, reaching 67.8% on CUB-200-2011 and 85.2% on Cars-196 at Recall@1.