Minimum Intervention Assessment in Historic Building Conservation: An Entropy-Weighted Intervention Intensity Index and Machine Learning Analysis at Caishen Temple, Xinzhou

Conservation interventions in historic buildings require quantitative assessment to balance preservation needs and minimal intervention principles. This study presents a quantitative framework for minimum intervention assessment of heritage conservation, based on 133 documented interventions at Caishen Temple in Xinzhou across four periods: 1992, 2006, 2016, and 2017. Four dimensions were defined: Extent of Intervention (EI), Reversibility (RE), Information Loss (IL), and Necessity (NE). Expert scoring on a five-point Likert scale yielded high inter-rater reliability (ICC: 0.79–0.88). The entropy weight method was then used to derive data-driven weights from the expert-scoring matrix, IL=0.3149, EI=0.3025, NE=0.2572, RE=0.1253, and the Intervention Intensity Index (III) was calculated for each intervention. A random forest model was further developed as an exploratory cross-check, with 19 problematic interventions labelled as y=1 and 114 normal interventions as y=0. Target labels were defined through a dual-source procedure combining SSIM- and HSV-based image-similarity assessment for interventions with paired pre- and post-restoration photographs and explicit textual evidence from conservation records for interventions without paired photographs. Feature importance and SHAP analyses indicated that reversibility and necessity had the greatest discriminative importance for distinguishing problematic from normal interventions in this dataset, whereas the entropy-based ranking assigned higher weights to information loss and extent of intervention. This descriptive contrast, based on only four dimensions, highlights the distinction between data dispersion and discriminative capacity. Spatiotemporal analysis indicated that roof and rafter components exhibited the highest intervention intensity, while 1992 interventions contained the largest proportion of problematic practices. This study combines entropy-derived weighting with a machine-learning-based exploratory cross-check, providing a transparent, case-based framework for evidence-based conservation decision-making at historic heritage sites.