[Objective] This paper builds a large-scale knowledge graph for scientific research, which meets the needs of sci-tech information services and improves the data consistency of traditional models. [Methods] First, we proposed an implicit knowledge graph construction method. Then, we used the identification tools for entity feature fields and implicit relationships to continuously update entities and discover entity relationship. [Results] We examined the proposed model with big data platform for PB-level sci-tech literature. Once there are changes in the entity data, the implicit knowledge graph will only update the entity data and will not modify their relationship. The model could retrieve all scholars from one institution through the predefined interface, and the average processing time was one hundredth of the triple-type knowledge graph. [Limitations] It is difficult to solidify the situation not satisfying the implicit relational data structure, and the entity data must be stored in a technical cluster with search engine. [Conclusions] The proposed method could effectively improve the data consistency issue due to changes in entity information. It helps us construct large-scale scientific research knowledge graph, which benefits the management, dissemination and utilization of sci-tech knowledge.