Since Google introduced the concept of Knowledge Graphs (KGs) in 2012, their construction technologies have evolved into a comprehensive methodological framework encompassing knowledge acquisition, extraction, representation, modeling, fusion, computation, and storage. Within this framework, knowledge extraction, as the core component, directly determines KG quality. In military domains, traditional manual curation models face efficiency constraints due to data fragmentation, complex knowledge architectures, and confidentiality protocols. Meanwhile, crowdsourced ontology construction approaches from general domains prove non-transferable, while human-crafted ontologies struggle with generalization deficiencies. To address these challenges, this study proposes an Ontology-Aware LLM Methodology for Military Domain Knowledge Extraction (LLM-KE). This approach leverages the deep semantic comprehension capabilities of Large Language Models (LLMs) to simulate human experts’ cognitive processes in crowdsourced ontology construction, enabling automated extraction of military textual knowledge. It concurrently enhances knowledge processing efficiency and improves KG completeness. Empirical analysis demonstrates that this method effectively resolves scalability and dynamic adaptation challenges in military KG construction, establishing a novel technological pathway for advancing military intelligence development.