病害勘查是研究石质文物劣化机制并制定保护措施的基础。然而，传统基于摄影测量的文物保护病害识别方法存在主观性过强、过度依赖工程师经验且效率低下的问题。为解决这一问题，本研究以大足峰山寺摩崖造像为对象，提出了一种基于高光谱成像技术的智能识别方法，用于石质文物病害识别。研究采用400-1000nm 波段高光谱成像系统，通过 Savitzky-Golay 平滑滤波、归一化及包络线去除预处理提升信噪比，运用竞争性自适应重加权采样 CARS、随机蛙跳 RFLA 和连续投影算法 SPA 提取关键特征波长以减少冗余，对比结合不同特征波长的随机森林 RF 分类模型性能。研究结果发现预处理后光谱特征分离度得到提升，噪声消除率达 89%。CARS-RF 模型综合表现最优，可精准区分剥落、粉化等病害类型，微观区域边界识别误差低于其他模型，识别结果与现场调查高度吻合。量化分析表明 ：轻微风化为主要病害，占比达 31.71% ；粉化和剥落次之，分别占比 28.65%、23.02% ；彩绘残余占比最低，仅有 16.62% ；与现场调查记录高度吻合。该方法为石质文物的数字化保护提供了可靠技术支撑，其高光谱特征提取策略可推广至同类文化遗产的检测中。

Condition assessment serves as the foundation for studying the deterioration mechanisms of stone heritage and formulating conservation measures. However, traditional photogrammetry-based identification methods for heritage deterioration often suffer from excessive subjectivity, over-reliance on engineer experience, and low efficiency. To address these issues, this study proposes an intelligent identification method based on hyperspectral imaging technology, using the rock carvings at Fengshan Temple in Dazu as a case study. A hyperspectral imaging system covering the 400-1000 nm wavelength range is employed. Signal-to-noise ratio is enhanced through preprocessing techniques including Savitzky-Golay smoothing filter, normalization, and continuum removal. To reduce spectral redundancy, key wavelength features are extracted by using Competitive Adaptive Reweighted Sampling (CARS), Random Frog Leap Algorithm (RFLA), and Successive Projections Algorithm (SPA). The performance of Random Forest (RF) classification models incorporating these features is compared. Results show that spectral feature separability has significantly improved after preprocessing, with a noise reduction rate of 89%. The CARS-RF model achieves the best overall performance. It can accurately distinguish deterioration types such as flaking and powdering exhibiting lower boundary recognition errors in micro-regions compared to other models, and its identification results are highly consistent with field survey findings. Quantitative analysis reveals that mild weathering is the predominant deterioration type, accounting for 31.71%, followed by powdering (28.65%) and flaking (23.02%). Painted residue weathering is least prevalent, at only 16.62%. These proportions closely match the field investigation records. This method provides robust technical support for the digital conservation of stone heritage, and its hyperspectral feature extraction strategy can be extended to the inspection of similar cultural heritage.