Aimed at the problems that data processing capability and decision-making speed are limited in kill net combat system in complex battlefields, as well as the problems that requirements of data are large, generalization ability is weak, and interpretability is low in the existing AI decision-making methods, this paper proposes a decision-making method based on knowledge-enhanced Large Language Models (LLMs). The method is to optimize the kill net decisions by directly leveraging LLM to understand the battlefield situation and semantics, through integrating three major modules, i.e. from the environmental state map ping, the knowledge-enhanced LLM decision-making, and the decision text to the agent behavior conversion. The results show that this method can effectively understand battlefield situations and formulate decision schemes, significantly reducing the dependence on large amounts of data while ensuring good interpretability.