Predictive Modeling of Pre-Weathered Chemical Content in Glass Artifacts

With the continuous development of cultural heritage protection technology, the study of weathering of cultural relics has become an important topic. In particular, glass products are easily affected by weathering due to the high reactivity of their components, resulting in changes in chemical composition. This paper proposes a prediction model for the chemical composition of glass products before weathering based on weathering rate. By statistically analyzing the chemical composition data of different types of glass cultural relics and combining the relationship between the chemical composition after weathering and the weathering rate, a prediction method for the chemical composition before weathering was established. This model can effectively handle missing and outliers in cultural relic data, and provide a scientific basis for cultural relic restoration by inferring the chemical composition before weathering. The results show that this method not only improves the prediction accuracy, but also provides a reliable tool for future cultural relic protection research. By combining data science and chemical analysis technology, the prediction and restoration of cultural relic weathering can be supported more accurately and systematically.