Document relationship extraction （DRE） is designed to identify the relationship between entities in multiple sentences， and entities may correspond to multiple mentions across sentence boundaries， in which the pronoun entity mention is a common grammatical phenomenon due to the connection between sentences， and is also an important factor affecting sentence reasoning. However， most of the previous studies focused on the relationship between common entity references， but paid little attention to the co-reference and relational capture of pronoun entity references. Therefore， we propose a contextual coreference entity dependency （CCED） model， that is， by integrating common entity and pronoun entity representation to build a context graph structure of co-referring entity dependency， and carry out global interactive reasoning between entity pairs on the graph， so as to model the interdependence of entity relations. We evaluated the CCED model in the public datasets DocRED， DialogRE and MPDD， respectively. The results showed that the CCED model improved Ign F₁ performance by 0.55% on the DocRED dataset compared with DocuNet-BERT， the best baseline model. And F₁ score performance increased by 0.35%. In terms of the DialogRE and MPDD datasets， the CCED model improved F₁ performance by 1.02% in DialogRE test sets and ACC performance by 1.19% in MPDD test sets compared with COLN， the best-performing baseline model. The experimental results verify the effectiveness of the new model for document-level relationship extraction.