人工智能生成内容中深度伪造信息层出不穷，急需精准诊断评估用户在面对深度伪造风险时的能力短板，从而为更好地提升深度伪造信息风险管理能力提供依据。文章基于风险管理理论，综合考虑风险识别、评价、应对三阶段过程，确立深度伪造信息风险管理能力指标体系，结合不确定性层次法和集对分析法构建同异反五元联系数风险管理能力评估模型，并通过实际应用实例进行分析验证。实证结果表明，当前用户深度伪造信息整体风险管理能力处于一般水平，但未来向更差水平发展的趋势较弱，能力下降风险较低。其中，用户深度伪造信息风险识别、评价以及应对能力均处于负向发展的反势区，但风险应对能力负向趋势最强，需采取措施进行优化。

With the continuous proliferation of deepfake information in AI-generated content, it is imperative to accurately identify and assess users' deficiencies in coping with such risks, which provides a critical foundation for enhancing users' capabilities in managing deepfake information risks. Drawing on risk management theory, this paper integrates the three fundamental stages-risk identification, evaluation, and response-to construct a comprehensive indicator system for assessing deepfake information risk management capability. A risk assessment model is developed based on the five-element connection number framework of similarity, difference, and opposition, combining the Uncertainty Hierarchy Method with Set Pair Analysis. The model's effectiveness is demonstrated and validated through empirical case analysis. The empirical findings indicate that users' overall risk management capabilities regarding deepfake information remain at an average level, with only a weak tendency toward further decline, suggesting a relatively low risk of capability deterioration. Specifically, users' capabilities in risk identification, evaluation, and response are all situated within the anti-trend zone of negative development. Among these, the downward trend in risk response capability is the most pronounced, underscoring the need for targeted measures to optimize this dimension.