Abstract: Focusing on the requirement analysis and affective design of smart cockpits for intelligent connected vehicles (ICVs), a "usage-comment-psychology" Kansei engineering analysis framework is proposed to optimize the functional configuration of in-vehicle systems to enhance the users' affective experiences. The proposed framework is established by deeply mining the web comment data and behavioral log data generated by users to extract the characteristics of the psycholinguistic demand. Multimodal data are adopted in the proposed Kansei engineering analysis framework to combine large language models with econometric models. Through an incrementally pre-trained large language model, three types of comment demand feature labels, namely, the application type, the comment intention, and the sentiment polarity, are predicted from user comments with 95.22% accuracy. These features are incorporated with the psycholinguistic features derived from TextMind and the usage behavior features into a structural equation model to expand the analytical scope of requirements engineering, providing deeper insights int users’ affective requirements for smart cockpits. As an application example, the behavior of 2,048 ICV drivers of a partner new energy vehicle company are tracked for four months. Results verify the effectiveness of the method in revealing users' application requirements and sentiment tendencies. Compared with the linear regression model, the "usage-comment-psychology" model demonstrates superior goodness-of-fit and further reveals the implicit relationship between behavior and psycholinguistic features. Driven by the user big data, the method realizes automatic extraction and relationship interpretation of multiple dimensional features such as behavior, opinion and psychology, providing a feasible solution for revealing the mechanism between users' complex behavior and design requirements. It also provides a valuable reference for manufacturing enterprises to understand customer requirements and guide the iteration direction of their products.