In the context of artificial intelligence, deepfake information subverts existing cognition, triggers trust crises, and intensifies social conflicts. This article accurately captures the key elements and core paths that influence the willingness to share deepfake information. It can effectively improve the accuracy and effectiveness of deep forgery information governance. Based on this, integrating information ecology theory and SOR theory, this article collected 446 valid samples using a questionnaire survey method. It uses multiple linear regression analysis to explore the causal relationship between individual variables and outcome variables. On this basis, this article uses the fsQCA method to explore the combined effects of multiple variables on the outcome variables, in order to reveal the complex causal driving path of the willingness to share deepfake information. This study found that the public opinion environment, emotional appeals, perceived information credibility, and information authority have a positive impact on the willingness to share deepfake information, while the regulatory environment and information harmfulness have a negative impact on the willingness to share deepfake information. None of the variables constitute a necessary condition for high-level/low-level deepfake information sharing willingness. This article obtained 9 configuration paths that drive the willingness to share high-level deepfake information and 10 configuration paths that drive the willingness to share low-level deepfake information. Based on this, this article distills three high-level driving modes of willingness to share deepfake information (low-harm driving mode, authority-induced mode, and media-boosted mode) and two low-level inhibitory modes of willingness to share deepfake information (harm-inhibiting mode and regulatory-constraint mode).