Brownfield-transformed urban parks, particularly those derived from industrial heritage, play a critical role in both cultural preservation and public-space provision. However, existing studies often rely on linear models and general urban contexts, limiting their ability to capture nonlinear, interaction-driven perception and translate analytical results into design-oriented insights. To address this gap, this study develops an interpretable data-driven framework integrating NLP (natural language processing) with explainable machine learning. Using social media reviews from Shougang Park in Beijing, built environmental elements are identified and structured into four dimensions—Accessibility, Safety, Comfort, and Enjoyment. An XGBoost model combined with SHAP analysis is employed to examine variable importance, nonlinear relationships, and interaction effects. The results reveal that visitor satisfaction is governed by heterogeneous and nonlinear relationships rather than independent additive effects. Several variables exhibit threshold-like, diminishing, and inverted-U-shaped patterns, indicating sensitivity to intensity ranges. More importantly, spatial perception emerges from the nonlinear coupling of multiple elements, forming four representative interaction types: compensatory, inverted-U-shaped, context-dependent, and threshold-like relationships. Key interactions are concentrated around industrial landscape, leisure activities, and supporting facilities. Building on these findings, the study translates interactions into design-oriented strategies, emphasizing synergistic configuration, functional balance, moderated development intensity, and context- sensitive programming. By linking interpretable machine learning with spatial design, this research advances an interaction-oriented paradigm and provides a transferable framework for satisfaction-informed evaluation and optimization of brownfields.
Wang et al. (Tue,) studied this question.