地球科學中許多自然現象具有潛藏的時間循環特徵，例如地震活動週期、颱風生成與移動規律以及社群媒體關注趨勢的形成等。本研究以「時間序列規律」的觀點出發，探討科學研究主題是否同樣呈現類似的演化與循環特徵。本研究蒐集全國科學展覽地球科學類（涵蓋天文、大氣、海洋與地質）歷年投稿與得獎作品資料，建立長期的研究主題資料庫，並透過資料分析方法，探索地球科學研究主題在教育場域中的發展趨勢與知識網絡結構。
本研究在方法上，整合社群網絡分析（Social Network Analysis, SNA）、PageRank 節點權重分析、時間序列模型（Gated Recurrent Unit, GRU）、樞紐分析（Pivot analysis）與關聯規則學習（Apriori）等方法，建構多模型整合分析架構。首先透過熱力圖與關聯規則進行整體趨勢掃描，再以社群網絡結構辨識研究主題之間的連結關係與核心節點角色，進一步結合時間序列模型分析研究主題在不同年代的演進變化。本研究結果顯示，部分專家與應用類別組合，即地球物理學/2GP – 天文與行星科學/1APS、大氣科學/3AS – 氣候變遷與環境科學/3CCE，在長時間尺度上呈現穩定的研究動能，其中 3CCE 類別在近年有逐步增加的趨勢。透過整合 2014~2025 年時間切片、學籍別、關鍵字與研究類別等多維資料，建立了多維綜合指標，用以評估研究主題與教師節點在知識網絡中的影響力與結構位置。
進一步分析師生合作網絡亦發現，部分教師在不同研究類別之間扮演跨領域橋樑角色，促成研究主題的交流與學生探究能力的培養。同時，本研究將研究主題變化與重大自然事件，例如；地震、聖嬰暨南方振盪（El Niño-Southern Oscillation, ENSO）與颱風等進行時間對照分析，結果顯示科展研究主題的產出與自然環境事件之間，存在時間對照呈現部分同步或滯後線索，據此提出『事件強迫—知識回應』作為待驗證分析假說。整體而言，本研究建構一套結合社群網絡與時間序列分析的地球科學知識演化分析架構，不僅能解釋科展研究主題的長期發展脈絡，也可作為未來熱門研究方向與規劃科學教育發展的參考依據之一。

Natural phenomena commonly exhibit temporal cyclicity and evolutionary characteristics. This study integrates Social Network Analysis (SNA), PageRank node weighting, Gated Recurrent Unit (GRU) time-series modeling, Pivot Analysis, and Apriori association rule learning to construct a multi-model analytical framework for exploring the evolution and interaction of Earth science research topics. The results indicate that specific expert–application category combinations, such as Geophysics/Astronomy and Planetary Sciences and Atmospheric Sciences/ Climate Change and Environmental Science, show comparatively persistent activvity long-term research momentum, while the Climate Change and Environmental Science category has shown a comparatively increasing presence in recent years. Collaboration network analysis further reveals that certain teachers act as interdisciplinary bridges across research categories, facilitating knowledge exchange and fostering students’ inquiry abilities. Through temporal comparison with major natural events, the study suggests a dynamic “event-driven versus knowledge-response” interaction pattern and proposes an integrated framework combining social network and time-series analyses to investigate the evolution of Earth science knowledge.

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