Facing the characteristics of current industrial product systematization, user demand information complexity and information content digitization, this paper is planned to explore the innovation of user demand insights in product design through the application of statistical analysis and big data mining methods in user demand acquisition and processing method. The case analysis and factor analysis methods were used to extract the design factors of the target product, and saliency analysis was performed through multiple regression analysis; then the product online review mining and sentiment analysis methods were used to obtain and process user explicit needs for product characteristics. And from the perspective of scenario analysis, the behavioral pain points and hidden needs in the use scenario were obtained. Finally, corresponding to the product characteristics and user demand indicators in the use scenario, the product attribute requirements and product scenario adaptability requirements were derived. The comprehensive application of SPSS and online comment analysis methods can solve the problems of insufficient method application and lack of objectivity in current product user demand insights. It helps designers to accurately obtain product design goals when faced with complex product systems and user-demand design tasks. It also empirically analyzes the needs of heavy truck life cabin users to obtain product design requirements that adapt to the environment of the heavy truck life cabin.