With the growing adoption of data-driven workflows and the need to compare numerous interior design alternatives in housing renewal, scalable and consistent assessment of interior space quality is increasingly important; however, current practice still depends on manual scoring and expert judgment. To address this gap, we propose an automation-ready framework that evaluates interior space quality from visual data. We construct the Functionality–Healthiness–Aesthetics Spatial Interior Dataset-10K (FHASID-10K) with 13,962 images for systematic validation. Three sub-models quantify functionality via space utilization and circulation smoothness, healthiness via detection of health-related visual elements, and aesthetics via semantic visual representations with regression-based prediction. Dimension scores are standardized and fused using the analytic hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS) to produce a comprehensive score for ranking and grading. Experiments show stable score distributions and clear differentiation across space categories and style–space combinations. A gradient-boosted decision tree (GBDT) surrogate reconstructs the fused score with high accuracy (test R2 = 0.9992; MSE = 1.1 × 10−5), and human-subject evaluation shows strong agreement with overall-quality ratings (r = 0.760, p < 0.001). Overall, the framework enables scalable benchmarking, scheme comparison, and decision support.
Wang et al. (2026) studied this question.