Combining NMF and DFNN for Data-Driven Kansei Design of New Energy Vehicle Rear-End Styling

Against the background of increasing styling convergence in the new energy vehicle (NEV) market, rear-end styling has gradually become a key visual interface for communicating brand identity, shaping product differentiation, and eliciting users’ Kansei cognition. However, existing Kansei design studies on automotive styling have mainly focused on whole-vehicle forms or front-face morphology, while systematic modeling methods for local rear-end styling remain limited. Under small-sample conditions, the nonlinear mapping between the Kansei semantic space and styling parameters also faces the risk of overfitting. To address these issues, this study proposes a data-driven Kansei Engineering (KE) framework integrating non-negative matrix factorization (NMF), grey relational analysis (GRA), and deep feedforward neural network (DFNN), aiming to achieve a continuous translation from Kansei need identification to parametric scheme generation for rear-end styling. First, the original seven-dimensional Kansei evaluations were aggregated into three latent Kansei dimensions through the non-negative low-rank decomposition of NMF. Second, GRA was used to screen key morphological features and reduce modeling complexity at the feature level. Third, DFNN and random forest (RF) were constructed as prediction models, and DFNN showed better average test RMSE and R2 than RF. Finally, the optimal codes predicted by the DFNN were transformed into design schemes constrained by morphological coding, and their consistency in expressing the target Kansei images was verified, thereby establishing an engineering constraint-oriented and interpretable decoding pathway distinct from free-association-based Kansei design. The ablation experiment indicates that the performance advantage of the proposed framework does not arise solely from DFNN, but from the mathematical coupling among NMF-based semantic aggregation, GRA-based feature screening, and DFNN-based nonlinear mapping. This framework reformulates Kansei design as a hierarchical decomposition and modeling process, establishing a data-driven decision-support tool jointly driven by mathematical algorithms and artificial intelligence for NEV rear-end styling design.