Addressing the challenges of fragmented knowledge and difficulties in analyzing cross-stage linkages within the lithium resource sector, this study constructs a full-cycle knowledge graph to map the industry's value-added pathways. The graph encompasses the entire life cycle, including exploration and development, production and extraction, material manufacturing, end-use application, and recycling and disposal. A top-down methodology was employed, beginning with the development of a domain-specific ontology that defines 17 entity types and 12 relation types. Using papers on lithium resources from the China National Knowledge Infrastructure (CNKI) as the primary data source, we extracted 6 685 knowledge triples by applying large language models (LLMs) and prompt engineering techniques, and subsequently utilized the Neo4j graph database for storage, management, and visualization.Through its systematic modeling of the lithium resource value chain, the knowledge graph provides critical support for deep analyses, including supply chain risk identification and cross-stage knowledge discovery.