This study investigates users' continuance intention towards AI painting applications by extending the Expectation Confirmation Model (ECM), Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and Flow Theory. A comprehensive research model was proposed, and data were collected from 443 users with AI painting experience. Structural equation modeling was used to test the hypotheses. The key findings include: 1) Confirmation significantly and positively predicts satisfaction and social impact. 2) Personal innovativeness significantly affects confirmation. 3) Satisfaction, flow experience, and social influence directly and positively predict intention, with social influence having the most significant impact. 4) Habit negatively moderates the relationship between social influence and continued intention to use. These findings provide insights into users' appropriate use of AI painting and guide the development of AI painting applications. The study also highlights the importance of social influence, satisfaction, and flow experience in users' continued use of AI painting. Additionally, it shows that habit can negatively moderate the relationship between social influence and continued use. The research contributes to the understanding of user behavior in AI painting and offers practical implications for the development and promotion of AI painting technologies.This study investigates users' continuance intention towards AI painting applications by extending the Expectation Confirmation Model (ECM), Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and Flow Theory. A comprehensive research model was proposed, and data were collected from 443 users with AI painting experience. Structural equation modeling was used to test the hypotheses. The key findings include: 1) Confirmation significantly and positively predicts satisfaction and social impact. 2) Personal innovativeness significantly affects confirmation. 3) Satisfaction, flow experience, and social influence directly and positively predict intention, with social influence having the most significant impact. 4) Habit negatively moderates the relationship between social influence and continued intention to use. These findings provide insights into users' appropriate use of AI painting and guide the development of AI painting applications. The study also highlights the importance of social influence, satisfaction, and flow experience in users' continued use of AI painting. Additionally, it shows that habit can negatively moderate the relationship between social influence and continued use. The research contributes to the understanding of user behavior in AI painting and offers practical implications for the development and promotion of AI painting technologies.