Large Language Models for Military Force Recommendation：Applications and Considerations
JIAO Pengbo1 GONG Zhixing2 LUO Zhihao1 FAN Changjun 1, * SHI Jianmai1
1. Laboratory for Big Data and Decision, College of Systems Engineering, National University of Defense Technology, Changsha 410073, China;
2. Unit 31002 of PLA, Beijing 100091, China
Abstract：Large language models（LLMs） have achieved groundbreaking advancements in artificial intelligence and are poised to revolutionize military applications. Traditional military force resource recommendation systems rely on target IDs, force resource IDs, and historical interaction data for predictions, but suffer from poor data utilization, weak transferability, and cold-start problems. To enhance the capability and efficiency of military force recommendation systems and improve the scientific rigor of target strike planning decisions, this study analyzes LLMs applications through the lens of the military recommendation paradigm. We examine LLM’s potential across feature engineering, feature encoding, and scoring prediction stages, demonstrating its evolution from an auxiliary tool to a comprehensive
replacement. Comparative analysis of different application scenarios reveals distinct advantages. Finally, we summarize both opportunities and challenges for LLMs implementation in military recommendation systems. This research anticipates LLMs enabling more intelligent military decision-making methods while compensating for current technological and force structure limitations through AI-driven advantages.
焦鹏博，龚治兴，罗志浩， 范长俊， 石建迈. 大模型在兵力推荐中的应用与思考[J]. 指挥与控制学报, 2025, 11(2): 137-145.
JIAO Pengbo，GONG Zhixing，LUO Zhihao， FAN Changjun,SHI Jianmai. Large Language Models for Military Force Recommendation：Applications and Considerations. Journal of Command and Control, 2025, 11(2): 137-145.
2025, Vol. 11 
