Aiming at problems of ignoring entity mention differences and lack of complexity calculation paradigm for entity-pair relation extraction in the existing entity representation learning for Document-level Relation Extraction （DocRE） tasks， a DocRE model based on Entity Representation Enhancement （DREERE） was proposed. Firstly， an attention mechanism was used to evaluate the differences of entity mentions in determining different entity-pair relations， so as to obtain more flexible entity representations. Secondly， the entity-pair sentence importance distribution computed by the encoder was used to evaluate the complexity of entity-pair relation extraction， and the two-hop information among entity-pairs was used selectively to enhance entity-pair representations. Experiments were carried out on the popular datasets DocRED， Re-DocRED and DWIE. The results show that DREERE model improves the F1 value by 0.06， 0.14， and 0.23 percentage points， respectively， and the ign-F1 （F1 score calculated by ignoring the triples that appear in the training set） value by 0.07， 0.09 and 0.12 percentage points， respectively， compared to the optimal baseline models such as ATLOP （Adaptive Thresholding and Localized cOntext Pooling） and E2GRE （Entity and Evidence Guided Relation Extraction）， indicating that DREERE model is able to acquire semantic information of entities in documents effectively.