<p>Eliciting evolutionary requirements is essential for the continuous improvement of software systems. Traditional approaches rely on user feedback from forums and social media; however, these methods often suffer from low engagement rates and subjective biases. In contrast, user interaction behavior data offers more objective and comprehensive insights into user needs throughout task execution. Despite its advantages, the ambiguous relationship between user behaviors and requirements presents significant challenges for behavior-driven requirement inference. This paper proposes a novel approach for eliciting evolution requirements based on user interaction behavior. We first employ the conceptual model and goal model to normalize multi-modal behavior data and associate it with goal contexts. This structured representation reduces complexity and establishes a foundation for further analysis. Next, we extract key behavioral metrics through statistical analysis to quantify task difficulty from a user experience perspective, providing a basis for prioritizing inferred requirements. Finally, pattern mining techniques and large language model are applied to derive evolutionary requirements, identifying meaningful behavioral patterns to strengthen the connection between user behavior and requirements while enhancing automation and effectiveness by large language model inference. A case study with 20 participants was conducted to validate the proposed method. The results demonstrate that the approach successfully captures 95.6% of explicitly stated user requirements and uncovers additional valuable insights. These findings underscore the method’s effectiveness and usefulness in enhancing software design optimization.</p>