Abstract
<jats:p>The rapid evolution of smart wearable textiles demands advanced analytical tools to identify emerging technological opportunities. This study employs graph network analysis of global patent data to map the innovation landscape and forecast convergence pathways in wearable e-textiles. Each record was analyzed using International Patent Classification (IPC) codes to construct a co-occurrence network, which was further examined through centrality measures, including degree, betweenness, closeness, and eigenvector. These hubs highlight strong innovation activity and potential convergence pathways, including AI-enabled health monitoring fabrics, sustainable e-textiles, biocompatible sensors, and self-powered textiles. The study contributes a predictive framework for mapping innovation hot spots in wearable textiles, offering strategic insights for R&D and industry stakeholders. Limitations include reliance on IPC classification and exclusion of full-text analysis, suggesting future research should integrate semantic text mining and commercialization data. Overall, graph-based patent analysis demonstrates value in anticipating technological evolution in next-generation wearable e-textiles.</jats:p>