The work aims to fully leverage user reviews on online shopping platforms, identify the optimal combination of morphological factors for guiding product modeling design and address the challenge of aligning products with users' emotional needs. With rice cookers as an example, web crawling techniques were employed to collect user reviews from an online shopping platform and quantify them according to the TF-IDF algorithm. A principal component analysis was then conducted to select emotional evaluation indices, and the target product was decomposed into multiple main structures with the help of a morphological analysis. By constructing a BP neural network-based user emotional evaluation model and traversing all possible combinations of morphological factors, the highest-ranking factor combination for each evaluation index was determined according to the model. The mean square error of the model was 0.004 9 with a coefficient of determination of 0.928 7, indicating its high accuracy in meeting requirements. Furthermore, a questionnaire survey method validated that the prediction results held reference values. In conclusion, the constructed BP neural network model demonstrates its ability to efficiently determine the best combination of morphological factors. Utilizing user reviews from online shopping platforms as training samples overcomes challenges associated with time-consuming manual collection or questionnaire surveys such as high costs, slow market response times, and uneven sample distributions. Ultimately, the user emotional evaluation model's prediction results provide valuable guidance for designers seeking accurate product modeling schemes based on users' preferences.