Microbe-drug interactions, which refer to the effects of drugs on microorganisms, play a crucial role in the realm of studying antibiotic-resistant bacteria and the development of antimicrobial agents. With the rapid progress in biomedical field, numerous experimental results containing validated microbe-drug interactions have been available in scientific articles. However, since failing to employ domain knowledge, traditional natural language processing methods encounter challenges in accurately identifying microbe and drug entities. Moreover, the unstructured characteristics and semantic complexity of biomedical literature pose difficulties for conventional text mining approaches to accurately grasp the syntactic features. In this paper, we present a novel microbial-drug relation extraction model called D-GCN, in which dual graph convolutional networks are used. Specifically, the drug database Drugbank is leveraged as external domain knowledge, while the graph convolutional network-based model SemGCN is utilized to learn meaningful features from biomedical texts. In addition, the attention graph convolutional network A-GCN is introduced to capture crucial syntactic features contained in texts. The experimental results show that the proposed model achieves better performance over the selected baseline models, which means D-GCN can not only accurately recognize microbial and drug entity representations, but also effectively identify the microbe-drug interactions.