[Purpose/significance] This article designs targeted scene governance strategies to respond to complex gov⁃ernance needs. It improves the refinement and scenarization level of the governance of deepfake events public opinion information. It breaks through the limitations of the vertical depth and adaptation accuracy of existing governance strate⁃gies. [Method/process] By integrating risk management theory and scenario theory, this article takes the perception of public opinion information risk in deepfake events as the dependent variable, and forgery technology, public aware⁃ness, media dissemination, degree of harm, scope of impact, response capability, and regulatory norms as independent variables. It has constructed a model of influencing factors and causal driving factors for the perception of public opin⁃ion information risk in the AIGC era of deepfake events. [Result/conclusion] In personal privacy events, none of the variables constitute a necessary condition for high-risk perception. This article has obtained 16 configuration paths for
high-risk perception of public opinion information in deepfake events. It has refined three governance models: risk identification scene, risk assessment scene, and risk response scene, and proposed scene governance strategies for deepfake events public opinion information.