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<p>Analyzing the evolutionary trajectories of intelligent energy technology (IET) is critical for enterprises to refine their research and development (R&amp;D) strategies and for governments to set targeted policy priorities. Existing literature predominantly investigates the developmental status, innovative prospects, and industrial implications of IETs, yet overlooks their long-term evolutionary pathways. This paper adopts IET evolutionary trajectories as its core research subject and employs multiple analytical tools: the Girvan–Newman algorithm, Latent Dirichlet Allocation (LDA), and Subject–Action–Object (SAO) semantic analysis. Its theoretical innovations are twofold. First, this work centers on IET evolutionary trajectories and unpacks the intergenerational inheritance and long-term iterative patterns of relevant technologies, filling the research gap whereby prior scholarship fails to deliver systematic, dedicated examinations of such evolutionary logics. Second, it couples SAO semantic analysis with Main Path Analysis (MPA) to advance the identification framework for technological evolution. The core contributions of this study are summarized below. To begin with, this paper distinguishes three primary technological tracks and elaborates the hereditary logic within each subfield, alongside visualizing the complete technological evolution trajectories. The findings provide patent-based evidence to guide energy firms in optimizing R&amp;D allocation and capturing emerging innovation windows, while also furnishing empirical support for governments to design tiered, differentiated IET regulatory policies. Furthermore, this research establishes a unified analytical framework for technological evolution and proposes a lightweight, replicable paradigm to dissect segmented technological trajectories.</p>

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Research on the evolutionary trajectories of intelligent energy technology based on main path analysis and SAO semantic analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月4日

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  • Xuefeng Liu,
  • Yunxiao Zhang,
  • Wenyu Xi

摘要

Analyzing the evolutionary trajectories of intelligent energy technology (IET) is critical for enterprises to refine their research and development (R&D) strategies and for governments to set targeted policy priorities. Existing literature predominantly investigates the developmental status, innovative prospects, and industrial implications of IETs, yet overlooks their long-term evolutionary pathways. This paper adopts IET evolutionary trajectories as its core research subject and employs multiple analytical tools: the Girvan–Newman algorithm, Latent Dirichlet Allocation (LDA), and Subject–Action–Object (SAO) semantic analysis. Its theoretical innovations are twofold. First, this work centers on IET evolutionary trajectories and unpacks the intergenerational inheritance and long-term iterative patterns of relevant technologies, filling the research gap whereby prior scholarship fails to deliver systematic, dedicated examinations of such evolutionary logics. Second, it couples SAO semantic analysis with Main Path Analysis (MPA) to advance the identification framework for technological evolution. The core contributions of this study are summarized below. To begin with, this paper distinguishes three primary technological tracks and elaborates the hereditary logic within each subfield, alongside visualizing the complete technological evolution trajectories. The findings provide patent-based evidence to guide energy firms in optimizing R&D allocation and capturing emerging innovation windows, while also furnishing empirical support for governments to design tiered, differentiated IET regulatory policies. Furthermore, this research establishes a unified analytical framework for technological evolution and proposes a lightweight, replicable paradigm to dissect segmented technological trajectories.