随着生成式LLM能力的持续演进，其在社会认知模拟中的应用逐渐展现出范式级变革的潜力。传统社会仿真方法多依赖静态规则和简化行为模型，难以捕捉人类社会行为的动态演化和文化复杂性。基于LLM驱动的智能体因具备上下文理解与自然语言生成能力，成为建模社会认知机制的新兴工具，能够模拟诸如身份建构、价值判断、意图推理等复杂社会心理过程。简要介绍LLM的技术基础，指出使用LLM智能体与社会认知模拟的适配性；构建了一个涵盖属性建模、记忆管理、规划与行动的智能体社会认知建模框架；在模拟流程层面，提出“数据收集-智能体协同-多维评估”的技术链条，并探讨认知可解释性与模拟现实对齐等挑战；总结了当前在社会学、经济学、军事学等方面的应用进展，并讨论LLM社会认知模拟的前沿趋势与未来发展方向。

With the continuous evolution of the capabilities of generative LLMs, their application in social cognition simulation is demonstrating paradigm-shifting potential. Traditional social simulation methods predominantly rely on static rules and simplified behavioral models, making it difficult to capture the dynamic evolution and cultural complexity of human social behavior. LLM-driven agents, equipped with contextual understanding and natural language generation capabilities, are emerging as novel tools for modeling social cognitive mechanisms, enabling the simulation of complex socio-psychological processes such as identity construction, value judgment, and intentional reasoning. This paper briefly introduced the technical foundations of LLMs and highlighted their suitability for social cognition simulation. It constructed a framework for agent-based social cognition modeling, encompassing attribute modeling, memory management, planning, and action. At the simulation process level, the paper proposed a technical pipeline consisting of “data collection, agent collaboration, and multidimensional evaluation,” while delving into challenges such as cognitive interpretability and simulation-reality alignment.It summarized the current application progress in fields such as sociology, economics, and military science and discussed emerging trends and future directions for LLM-based social cognition simulation.