<p>Understanding users’ continuance intention toward Artificial Intelligence-Generated Content (AIGC) tools is crucial, given the increasing market competition and the significant economic and societal benefits associated with these technologies. Existing research on factors influencing continuance intention remains limited, especially from the perspective of technical features and perceived value. This study addresses this gap by developing a conceptual model based on the Stimulus-Organism-Response (SOR) model and perceived value theory. It hypothesizes that technical features (Creative Quality, Interaction Quality, Aesthetic Quality and Empathy) directly and indirectly influence continuance intention through perceived value (Perceived Emotional Value and Perceived Functional Value), and to further examine the relationships between empathy and other technical characteristic dimensions (Creative Quality and Interaction Quality). Using data from 476 Chinese users of text-based AIGC tools engaged in creative activities, Partial Least Squares Structural Equation Modeling (PLS-SEM) tests the conceptual model. The findings reveal that Creative Quality, Aesthetic Quality, Perceived Emotional Value, and Perceived Functional Value significantly influence continuance intention. Furthermore, Perceived Emotional Value mediates the impact of Creative Quality and Aesthetic Quality, while Perceived Functional Value mediates the effects of Creative Quality, Interaction Quality, and Aesthetic Quality on continuance intention. Empathy exerts a significant positive effect on enhancing the Creative Quality and Interaction Quality of AIGC tools. This study provides novel insights into mechanisms driving continuance intention from the perspective of technical features, offering strategic recommendations for AIGC tool developers to foster users’ continued engagement.</p>