In this paper, we propose distribution-aware hierarchical weighting (DHW) method for deep metric learning. First, we formulate the distributions of different classes according to the form of gaussian curves, and update distributions as the training process. Second, depending on the learnable distribution, we propose a loss function named distribution-aware loss with dynamic mining margins and hierarchical degrees of weights to make full use of samples. The experimental results show that our algorithm outperforms other state-of-the-art methods in terms of retrieval and clustering tasks. Code is available at https://github.com/zhuyinong1/DHW-master.