［目的/意义］ 网络舆情传播对社会稳定乃至国家安全构成严峻挑战。本研究旨在建立一个兼具机制解释与政策评估效用的网络舆情传播模型，为治理网络舆情、保障国家安全提供决策支撑。［方法/过程］ 研究在基础SEIQR模型基础上重新设定状态转移规则与信息传递模式，构造符合现实舆情传播链条的改进SEIQR模型。随后应用多主体建模方法，在微观个体交互层面还原网络舆情传播的全周期动态过程，并就政府公布事件真实信息、监管部门实施封禁措施、社会环境宣传教育引导等三类治理机制进行系统模拟，结合量化指标对比分析其干预效果。［结果/结论］ 在改进SEIQR模型中，相较于基准情境，“真相”“处罚”“教育”情境的感染者峰值数量依次递减、峰值出现时间依次延迟、始终未进入传播链条的人数依次增加，说明上述机制能够通过影响网络用户的状态转移过程，在不同程度上降低感染峰值、延缓传播速度、缩减影响范围。然而，三种情境下舆情传播周期均有延长，说明传播链条因政策干预而呈现一定程度的“长尾期”。研究揭示不同治理对策下的舆情演化规律，为预测舆情发展趋势、优化政策干预时机与科学评估治理策略提供量化支撑，对持续营造风清气正的网络空间、维护国家安全稳定具有较大的理论贡献与现实价值。

[Purpose/Significance] Online public opinion propagation poses a severe challenge to social stability and even national security. This study aims to establish an online public opinion propagation model that features both mechanistic interpretation and policy evaluation capability, thereby providing decision-making support for online public opinion governance and national security protection. [Method/Process] Based on the basic SEIQR model, this study reconfigured state transition rules and information diffusion patterns to develop an extended SEIQR model that better reflects real-world online public opinion propagation chains. An agent-based modeling (ABM) approach was then employed to reproduce the full-cycle dynamic process of online public opinion propagation at the micro-level of individual interactions. Furthermore, three categories of governance mechanisms—government disclosure of authoritative information, regulatory and platform-imposed account restrictions, and public education and guidance—were systematically simulated, and their intervention effects were comparatively analyzed using quantitative indicators. [Result/Conclusion] The results indicate that, compared with the baseline scenario, the infection peak size decreases sequentially across the “Truth”, “Penalty” and “Education” scenarios; meanwhile, the time at which the infection reaches its peak is progressively delayed, and the number of individuals who never enter the propagation chain sequentially increases. These findings indicate that the above mechanisms can reduce the infection peak, slow down the propagation speed, and narrow the scope of influence to varying degrees by affecting the state-transition process of online users. However, the propagation cycle is extended under all three scenarios, indicating that the propagation chain exhibits a certain degree of a “long-tail effect” under policy interventions. This study identifies the evolutionary patterns of online public opinion under different governance strategies, providing quantitative support for predicting public opinion development trends, optimizing the timing of policy interventions, and scientifically evaluating governance strategies. The findings offer significant theoretical contributions and practical value for fostering a clean and healthy online environment and safeguarding national security and social stability.