Exploring the relationships, mechanisms, and configurational paths between artificial intelligence literacy, health information literacy, and AIGC health information adoption is fundamental for optimizing human-AI interaction processes and standardizing information behaviors. This paper employs multi-stage experiments, structural equation modeling, and configurational analysis to examine the influence of artificial intelligence literacy, health information literacy, and AIGC health information adoption. Research findings indicate that artificial intelligence literacy and health information literacy have a positive impact on the adoption of AIGC health information, and perceived system quality and perceived information quality are important influencing paths, which further expand the information adoption model. At the same time, perceived knowledge consensus can also drive trust and the adoption of AIGC health information by acting on perceived system quality and perceived information quality, which reflects the multi-source information verification relationship of individuals towards AI-generated content. In addition, configuration analysis reveals that, apart from the trust-system dominant configuration adoption mode led by trust and perceived system quality, there are two other adoption modes, namely the ethics-oriented configuration and the evaluation-oriented configuration. This also provides a directional basis for regulating the adoption behavior of AIGC health information.