Machine Learning Applications in Cultural Artifact Analysis

Abstract

This paper discusses the role of machine learning in studying cultural artifacts, its capacity to uncover hidden patterns, categorize symbolic representation and enhance interpretive structures of different cultural settings.  The research involved supervised and unsupervised models that tested the classification level, the level of clustering, and the level of prediction of interpretability of the artifacts, which were texts, photographs, and historical documents.  The findings indicate that ensemble-based approaches, such as XGBoost and Random Forest, performed very well at classifying artifacts and were right over 90 percent of the time. The clustering algorithms such as k-means and the hierarchical methods, on the contrary, identified concealed cultural groupings with a high silhouette score of more than 0.75.  Also, the methods of explainability, i.e., SHAP values and rankings of the feature importance, demonstrated that the most significant elements of differentiating cultural artifacts were linguistic motifs, visual textures, and symbolic markers.  Regression approach-based methods have proven to have significant predictive relationships between the features of artifacts and cultural contexts, which emphasizes the value of the data-driven interpretation of the heritage studies.  The results all highlight the fact that machine learning does not just improve quantitative research but also enhances qualitative inquiry with scalable, replicable and interpretable insights.  This offers a theoretical basis of integrating the computational methods into cultural studies, thereby improving not only academic understanding, but also the electronic preservation of human history.