智慧運輸系統是許多國家積極發展的領域，我國的政策中也透漏對於智慧交通的重視，本研究將聚焦在智慧交通中的車聯網(Vehicle-to-everything, V2X)，透過分析車聯網專利來了解目前的市場技術走向，結合Beyond 5G (B5G)的核心技術用以呈現車聯網與B5G網路的統整性發展。研究利用文字探勘技術對車聯網專利進行知識管理以及商業化技術分析，加入機器學習來協助分析台灣產業現況。首先對專利進行關鍵字萃取和主題模型，進行初步的主題探勘，再藉由專利的引用關係建構出專利引用網路，利用圖注意力網路(Graph Attention Network, GAT)來聚合上述資訊，得到可用於分析車聯網專利主題的神經網路。後續分析中結合分群、關鍵字以及社群網路分析指標，以車聯網為主體來分析B5G中與其相關的技術重要性，後續會蒐集原始資料集以外的台灣相關技術專利，使用圖注意力網路預測專利主題，分析全球與台灣之間的技術發展差異。本研究方法可以提取全球專利中的核心研發技術，並分析特定領域的組成技術，協助廠商於車聯網技術領域上的研發策略擬定，優化研究時程並能夠快速呈現出國家之間的技術研發方向不同之處。

Intelligent transportation system (ITS) is a popular research field around the world. It can be shown from our country’s policies to understand the importance of smart transportation. This research focuses on the domain of Vehicle-to-everything (V2X) in ITS, understanding the current technology trend by analyzing the related patents. it integrates core technologies of Beyond 5G (B5G) to present a comprehensive development of V2X and B5G networks. Text mining technology is the main methodology to process the collected patents and does comprehensive knowledge management. The machine learning method is added to analyze current industry in Taiwan. The preliminary topic exploration and ontology construction with keyword extraction and topic modeling for collected patents. Using Graph Attention Network (GAT) to aggregate the patent information shown above. In the following analyze, we will collect related technology patents in Taiwan, use the graph attention network to predict the technical field of patents, and analyze the differences between global and Taiwan development. The proposed methodology in this study enables the extraction of core research and development technologies from global patents. It also facilitates the analysis of constituent technologies within specific domains. This assists industry stakeholders in formulating research and development strategies in the field of vehicle-to-everything (V2X) technology, optimizing research timelines, and providing a prompt depiction of divergent technological research directions among countries.

摘要 i
Abstract ii
圖目錄 v
表目錄 vii
壹、緒論 1
1.1研究背景 1
1.2研究目的 2
1.3研究架構 3
壹、 文獻回顧 4
2.1 關鍵字萃取 4
2.2 主題模型與分群演算法 5
2.3 本體論工程 8
2.4 文獻計量科學 9
2.5 圖學習與圖神經網路 10
2.6 社群網路分析 12
參、方法論 14
3.1建構本體及制定搜索策略 15
3.2關鍵字萃取 24
3.3主題模型 25
3.4建構專利引用模型 26
3.5圖注意力網路模型訓練 28
3.6分群 32
3.7技術分析 33
肆、分析結果 36
4.1全球車聯網技術宏觀分析 36
4.2專利文字探勘分析 40
4.3專利文字探勘驗證 60
伍、結論 68
參考文獻 71
附錄 78

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