Abstract: 【Purpose/significance】This study aims to analyze the factors influencing the adoption intention of Artificial Intelligence Generated Content (AIGC) by users, explore the architecture of the factors influencing AIGC adoption intention, and examine the path relationships among these factors. The findings of this study provide a theoretical basis for research on AIGC adoption intention and of⁃ fer relevant recommendations for the design of local AIGC products.【Method/progress】Grounded theory was employed, and semistructured interviews were conducted with 45 ChatGPT users. The Interpretive Structural Modeling (ISM) technique was utilized to ex⁃ plore and analyze the factors influencing AIGC adoption intention, followed by relationship mapping and validation using the Matrix Impact Cross-Reference Multiplication Applied to Classification (MICMAC) model.【Result/conclusion】Fifteen factors influencing AIGC adoption intention were identified across three dimensions: information quality, user perception, and technological characteris⁃ tics. An interpretive structural model was constructed to depict the relationships among these factors, highlighting the interaction paths between different levels of factors. The study provides insights and recommendations for the development of local AIGC products.【In⁃ novation/limitation】Through in-depth interviews and grounded theories, this study constructed a model of influencing factors of AIGC users' willingness to adopt, revealed the internal correlation path, and provided scientific and effective suggestions for local AIGC product design and theoretical research. However, the study sample was limited to ChatGPT users and may have some limitations.