Abstract: While driving transformations in online information order, generative AI models also generate disinformation risks characterized by an “objective+subjective” overlap. An analytical framework tailored to their technical characteristics is urgently needed. This study systematically deconstructs the technical logic of false information production in generative AI models based on their hybrid expert architecture, treelike reasoning patterns, localized semantic understanding attributes, and opensource ecosystem mechanisms. It analyzes the transmission mechanisms of false information risks across four stages: data input, algorithmic operation, content presentation, and cognitive dissemination. To address these risks: At the algorithmic level, implement a processbased oversight scheme encompassing “access reviewoperation disclosurepostevent verification”; at the presentation level, strengthen scenariobased, interactive “warning notice” labeling mechanisms; at the cognitive level, cultivate users' digital literacy and selfrestraintt capabilities to achieve effective information security governance in the AI era.

Key words: generative AI; DeepSeek; disinformation; technological governance; holistic governance

摘要： 生成式人工智能模型在推动网络信息秩序变革的同时，亦衍生出“客观+主观”叠加的虚假信息内容风险，亟需根据其技术特性构建分析框架.基于生成式人工智能模型的混合专家架构、树状推理模式、本土化语义理解属性、开源生态机制，系统解构其虚假信息生产的技术逻辑.从数据输入、算法运作、内容呈现、认知传播4个环节切入，剖析虚假信息风险的传导机制.针对诸多风险，在数据端应建立覆盖采集、清洗、存储的全周期质量管控体系；在算法端需实施“准入审查—运行披露—事后核查”的过程性监管方案；在呈现端要强化场景化、可交互的“告知注意”标识机制；在认知端需培育用户数字素养与指令约束能力，以此实现人工智能时代信息安全治理.

关键词: 生成式人工智能；DeepSeek；虚假信息；技术治理；全方位治理