The integration of , cloud computing, and big data technology is changing the business and management mode of the . However, the is characterized by a wide range of fields, complex environment, and many factors, which creates a challenge for efficient integration and leveraging of industrial big data. Aiming at the integration of physical space and virtual space of the current , we propose an (DT) system framework for the . In addition, an information model based on a (KG) is proposed to integrate complex and heterogeneous data and extract industrial knowledge. First, the ontology of the is established, and an entity alignment method based on scientific and technological achievements is proposed. Second, the bidirectional encoder representations from Transformers (BERT) based multi-head selection model is proposed for joint entity-relation extraction of information. Third, a relation completion model based on a relational graph convolutional network (R-GCN) and a graph sample and aggregate network (GraphSAGE) is proposed which considers both semantic information and graph structure information of KG. Experimental results show that the performances of the proposed joint entity-relation extraction model and relation completion model are significantly better than those of the baselines. Finally, an information model is established based on the data of 18 s in the field of basic machinery, which proves the feasibility of the proposed method.