Document-level translation methods improve translation quality with cross-sentence contextual information. Document contains structural semantic information, which can be formally represented as dependency relations between elementary discourse units (EDUs). However, existing neural machine translation (NMT) methods seldom utilize discourse structural information. Therefore, this study proposes a document-level translation method that can explicitly model EDU segmentation, discourse dependency structure prediction, and discourse relation classification tasks in the encoder-decoder framework of NMT, so as to obtain the representation of EDU enhanced by structural information. The representation is integrated with the encoding and decoding state vectors by gating weighted fusion and hierarchical attention, respectively. In addition, in order to alleviate the dependence on discourse parsers at the inference phase, the multi-task learning strategy is applied to guide the joint optimization of translation and discourse analysis tasks. Experimental results on public datasets show that the proposed method can effectively model and utilize the dependency structural information between discourse units to improve the translation quality significantly.