Chinese historical and cultural villages are known for numerous vernacular architectural heritages of a wide-ranging, widely distributed, and artificially intensive nature. In order to efficiently and rapidly conduct targeted conservation for heritage buildings, the prediction of the deterioration level of heritage buildings is a key prerequisite. In that respect, it is of the utmost importance to investigate the influence of different elements, such as the age and type of heritage building, on the deterioration of heritage buildings. This paper presents the feasibility of using logistic regression models to establish a heritage damage prediction and thereby confirm the buildings’ deterioration level. The model results show that age, type, style, and value play important roles in predicting the deterioration level of heritage buildings. Meanwhile, the correlation between the judging factors of heritage buildings and the deterioration level of the damage has also been revealed, which is expected to support subsequent conservation and restoration.

Anchang historical town, a historic Jiangnan water town in Shaoxing city, Zhejiang province, China, is home to well-preserved heritage buildings dating back to the Northern Song dynasty (960 – 1127). Despite preservation efforts, environmental factors, such as high humidity, heavy rainfall, and prolonged sunlight exposure continue to cause material degradation. Addressing the deterioration challenges requires comprehensive analysis and diagnosis to identify root causes and ensure the sustainable restoration of these cultural relics. To investigate the deterioration of historical buildings in Anchang historical town, the research team employed instrument-based detection, software simulations, data integration, and other methods. Two cultural heritage buildings were selected as case studies: the former site of the Bank of China at Wangjiaqiao (BCW building) and the residence of He Gongsheng at Siqiao (HGS building). In this study, a comprehensive analysis was conducted at both macro- and micro-levels, utilizing building models and incorporating environmental parameters. Macroenvironmental factors, such as regional temperature, sunlight, climate, humidity, and soil type were examined. Simultaneously, microenvironmental data, including wall materials, wall temperature, wall humidity, and sunlight exposure, were collected to analyze the impact of factors such as site selection, proximity to water bodies and vegetation, building layout and orientation, and window placement. By employing statistical analysis, inductive reasoning, and other techniques, the study identifies the mechanisms underlying wall damage and predicts future trends. Ultimately, this study aims to provide data-driven insights for the development of scientifically sound preventive conservation strategies for cultural relics and buildings, thus guiding future restoration efforts.