在學術研究領域，篩選和分析大量文字資料以發現重要模式的任務至關重要，而共詞分 析是一種複雜而強大的分析技術，可以揭示學術出版物中詞彙之間錯綜複雜的聯繫關係， 以幫助研究人員識別關鍵主題和趨勢。為了充分利用共詞分析的力量，我們開發了一個高效的資料管道，以實現資料搜尋、網路分析和視覺化等目的，此資料管道目標在提升多維網路資料流處理的效率，並結合了關聯式和非關聯式資料庫的優點;明確而言，關聯式資料庫用以儲存結構化數據，其中包括了共詞分析所需的元數據，為維度搜尋提供了快速查詢能力，而非關聯式的圖資料庫則用以處理和分析網路關係。此外，我們利用詞嵌入技術來評估對文字資料處理的結果，從而提高網路分析的準確性和可靠性，我們亦實作了網頁介面來呈現共詞網路分析以及其相關來源資料統計結果，還提供了互動功能，使用戶可以更深入地探索網路，實驗結果顯示我們的資料管道增強了結果的可解釋性和可用性，並有助於更直觀地發現潛在的研究主題和趨勢。

In the realm of academic research, the process of sifting through and analyzing vast amounts of written material to uncover significant patterns is crucial. Co-word analysis stands out as a sophisticated and effective technique that unveils the complex network of connections between words in academic publications, aiding researchers in pinpointing key themes and trends. To maximize the utility of co-word analysis, we propose to develop a comprehensive data pipeline that incorporates capabilities of data search, network analysis, and visualization. This pipeline is designed to enhance the efficiency of processing multi-dimensional network data streams and merges the benefits of both SQL and NoSQL databases. Specifically, the relational database within the pipeline manages structured data, including essential metadata for co-word analysis, offering rapid query capabilities for precise dimensional searches. Concurrently, the graph database handles the textual data of the co-word network, overseeing the processing and analysis of network relationships. To further refine our methodology, word embedding technology is employed to assess text processing outcomes, thereby boosting the accuracy and reliability of analyses. Our pipeline also includes advanced visualization tools that not only display the results of the co-word network analysis on a web interface but also allow users to interactively explore the network. These tools improve the interpretability and usability of the results, facilitating a deeper understanding and discovery of potential research topics and trends.

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