o explore potential product innovation directions hidden in different scenarios, this study proposes a framework for identifying product innovation directions based on user online reviews, from the “scenario-feature” association perspective. A vocabulary of scenarios and features is constructed using methods such as word frequency statistics and TF-IDF, and the co-occurrence patterns of “scenario-feature” terms are used to clarify the relationship between the two; a DeBERTa-BiGRU-MHA model is constructed for sentiment analysis, and the user attention intensity and sentiment scores for different “scenario-feature” pairs are calculated; We calculate users’ demand satisfaction scores and priority levels for different scenarios, and apply the KANO model and prospect theory to capture product innovation directions. Using automotive review data from the Autohome, Yiche and PCauto platforms for experimentation, our findings reveal that the DeBERTa-BiGRU-MHA model outperforms baseline models across multiple performance metrics. Furthermore, by comparing product parameter iterations, we have validated the feasibility of the proposed framework.