<p>Technological convergence reflects emerging innovation trends, and monitoring its dynamics is crucial for innovation-related decision-making. However, quantitatively identifying and visualizing significant strengthening or weakening in technology convergence remains challenging. This study proposes a method based on EPO DOCDB IPC data to detect such patterns. A global technology base map is constructed, on which global convergence overlay maps are developed to enable multi-level comparative analyses and visualization of prominent convergences. A Large Language Model (LLM)-based validation confirms the proposed method’s effectiveness in capturing substantial convergence trends, achieving an average significance percentage of 90% for strengthening convergence and 87% for weakening convergence. Using artificial intelligence (AI) as a case study, our results show that the method effectively reveals prominent global convergence patterns in the AI field, as well as the similarities and differences in convergence trends across countries and organizations. Globally, AI core technology computing shows the strongest convergence strengthening with life sciences and measurement technologies, while its convergence with communication technologies weakens. Nationally, both China and the U.S. promote AI–based life sciences convergence, more pronounced in the U.S., whereas China shows a more diversified convergence profile. Organizationally, Google, Microsoft, Huawei, and Baidu center their convergence strategies around computing, but their convergence targets differ, reflecting distinct strategic priorities and innovation trajectories. This method offers a novel analytical framework and visualization tool for monitoring technological convergence, aiding innovation policy and R&amp;D strategy formulation.</p>