User Need Prediction Based on a Small Amount of User-Generated Content—A Case Study of the Xiaomi SU7

(1) Background: In the current competitive market environment, accurately forecasting user needs is crucial for business success. By analyzing user-generated content (UGC) on social network platforms, enterprises can mine potential user needs and discern shifts in these needs, thereby enabling more efficient and precise product design that aligns with user needs. For newly launched products with a limited presence in the market, the scarcity of UGC poses a challenge to businesses seeking to predict user needs from small datasets. (2) Methods: To address this challenge, this paper proposes a model using correlation analysis (CA) and linear regression (LR) combined with multidimensional gray prediction (a CA-LR-GM (1, N) model) to help enterprises use small sample data to predict user needs. Using the UGC of the Xiaomi SU7 as a case study, this paper demonstrates the prediction of user needs for the vehicle and refines the prediction outcomes through an optimization design informed by the principle of optimal key feature distribution. (3) Results: The findings validate the feasibility of the proposed theoretical framework, offering a technical solution for the identification and prediction of user need trends. (4) Conclusions: This research puts forward strategic recommendations for enterprises regarding the optimization of their products.