Knowledge graphs are useful resources for many AI applications, but they often suffer from incompleteness. Previous work like TransE, TransH and TransR/CTransR, they regard relation as a translating from head entity to tail entity and the CTransR achieves state-of-the-art performance. In this paper, we propose a more fine-grained model named TransD, which is an improvement of CTransR. In TransD, we use two vectors to represent a named symbol object (entity and relation). The first one represents the meaning of a(n) entity (relation), the other one is used to construct mapping matrix dynamically. Compared with CTransR, TransD not only considers the diversity of relations, but also entities. TransD has less parameters and has no matrix-vector multiplication. In Experiments, we evaluate our model on two typical task including triplets classification and link prediction. Evaluation results show that our model outperforms the other embedding models including TransE, TransH and TransR/CTransR.