Abstract Ancient cultural ruins, as tangible evidence of human-environment interaction, provide invaluable historical insights and critical resources for addressing global challenges like climate change and population growth. However, most ruins are indistinguishable and challenging to identify, as they have merged with the surrounding sediments through prolonged natural accumulation and anthropogenic activities. Using SNV in conjunction with the ResNet50 model, this study proposes a novel method for achieving high-precision classification of spectral data from ancient human ruins. The ResNet50 model achieves a classification accuracy of 94.86% when used independently, whereas the accuracy improves to 96.60% when the ResNet50 model is combined with SNV. The model exhibits exceptional classification performance, even when trained with a limited number of spectral image samples from ancient ruins. The superiority of the SNV + ResNet50 model provides a pioneering and effective method for the rapid and accurate identification of ancient human relics using visible-near-infrared spectroscopy. Similar content being viewed by others Introduction Since their emergence, human beings have continuously interacted with the natural environment, accumulating a rich legacy of cultural remains throughout this prolonged and dynamic process. These ruins encapsulate the history of human cognition, adaptation, utilization, and transformation of nature, acting as a repository of extensive information on environmental evolution and human-land interactions. Therefore, ancient ruins can provide invaluable historical insights and serve as crucial resources for addressing global challenges such as climate change and population growth, thereby promoting the sustainable development of human society1,2,3. Under the influence of prolonged natural accumulation and anthropogenic activities, most human ruins have gradually become integrated into the surrounding environment4,5,6. Consequently, differentiating these remains from natural sediments and delineating their spatial morphology and distribution poses a significant challenge. The traditional manual investigation is not only inefficient but also heavily reliant on subjective experience for interpretation7,8,9. It is imperative to