隨著全球老齡人口的增加老齡化的議題逐漸變成各國政府乃至於各界急需解決的難題，使得智慧長照產業變得越來越受重視，了解智慧長照產業的發展趨勢不僅幫助企業在競爭的過程中取得優勢也可能讓智慧長照產業更快的進入成長週期。利用多種已存在技術結合為新技術的技術融合（Technology convergence）已經變成技術創新的主要途徑，加上智慧長照產業有結合多元應用的產業特性，更讓技術融合在智慧長照技術發展中的地位顯得重要。本研究主要的目的是利用鏈接預測（Link prediction）結合機器學習中的XGBoost模型，預測在智慧長照領域中即將發生的技術融合。本研究利用美國專利局（USPTO）資料，透過同時出現在一篇專利中的多種專利分類碼視為技術融合的象徵，並計算其社群指標，利用社群網路分析（Social network analysis），總集計算出12種指標後續利用主成分分析保留變異的同時減少維度。最後利用XGBoost，找出可能融合的技術。透過本研究可以提供產業內的公司在研發方向上的參考依據。

With the global aging population increasing, the issue of aging is gradually becoming a critical challenge that governments and various sectors urgently need to address. This has made the smart long-term care industry increasingly important. Understanding the development trends of the smart long-term care industry not only helps enterprises gain an advantage in the competitive process but also accelerates the industry's growth cycle. Technology convergence, which involves combining multiple existing technologies into new ones, has become a major pathway for technological innovation. Given the smart long-term care industry's characteristic of integrating diverse applications, technology convergence plays a significant role in its technological development.
The primary purpose of this study is to utilize link prediction combined with the XGBoost model in machine learning to predict impending technology convergence in the smart long-term care field. This study uses data from the United States Patent and Trademark Office (USPTO), considering the simultaneous appearance of multiple patent classification codes in a single patent as an indication of technology convergence. Community indicators are calculated using social network analysis, and a total of 12 indicators are aggregated. Subsequently, principal component analysis is used to retain variance while reducing dimensions. Finally, XGBoost is employed to identify potential technology convergence. This study can provide companies within the industry with a reference for research and development directions.

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