Complex interface structures hinder the manual classification of UI components. This paper introduces a prototypical approach for UI element classification that combines a custom browser extension for collecting structured metadata (e.g., DOM structure, CSS properties) with machine learning methods to assign UI components to subject-specific tasks. The approach enables reliable clustering without extensive manual annotation and supports the evolution of adaptive user interfaces. Evaluations in the domains of e-commerce and smart city applications demonstrate that a combination of structural and textual features provides sufficient discriminatory power. The method integrates with intelligent interaction systems and context-aware interfaces, reducing redundancy and addressing growing interface complexity. Similar to semantic segmentation techniques, it enables dynamic adaptation of interaction flows, contributing to improved user experience and functional interface design.