This study was conducted with the aim of designing an objective classification system for authentic Persian carpets. The findings provide a foundation for developing the Global Carpet Classification System (GCCS), which can lead to the creation of a quantitative valuation index and an integrated certification system. To this end, a comprehensive database of 99 museum carpet samples was compiled, encompassing 12 parameters related to structure, design, and raw materials.

To achieve these objectives, the K-Means clustering algorithm (using Euclidean distance and k = 3 based on the elbow method) was employed. The samples were grouped into three homogeneous clusters based on knot density (730 to 12,300 knots per square decimeter), material composition (silk/cotton in warp and weft), and pile type (wool/silk).

The main innovation of this research lies in transforming traditional and subjective criteria into objective indices, thereby enabling direct comparison between modern carpets and historical samples. This system not only enhances the accuracy of valuation but also contributes to pricing transparency, global market development, greater buyer trust, facilitating purchases aligned with buyers’ preferences for carpets from specific regions, and fostering confidence in sellers’ expertise.