用AI分析200篇文献，揭示工业5.0的模糊现状与核心趋势。
Uncovering Key Trends in Industry 5.0 through Advanced AI Techniques
- 融合LDA、BERTopic等算法挖掘文献主题
- 发现工业5.0概念分散且缺乏明确定义
- 展示AI在模糊领域中识别趋势的有效性
本文通过分析约200篇在线文章，利用LDA、BERTopic、LSA和K-means等算法，在不同配置下提取并比较文献中的核心主题。结果表明，尽管存在一些共性主题，但工业5.0涵盖范围极广，概念尚不清晰。研究指出，作为工业4.0的演进，工业5.0仍是一个宽泛且未明确定义的概念，导致其难以聚焦与有效应用。因此，为使其更具实用性，亟需进一步细化与明确。此外，研究证明，当文献量大且主题边界模糊时，成熟的AI技术仍可有效识别关键趋势，展现出在非结构化数据中挖掘深层洞察的潜力。
原文摘要 · Abstract (English)
This article analyzes around 200 online articles to identify trends within Industry 5.0 using artificial intelligence techniques. Specifically, it applies algorithms such as LDA, BERTopic, LSA, and K-means, in various configurations, to extract and compare the central themes present in the literature. The results reveal a convergence around a core set of themes while also highlighting that Industry 5.0 spans a wide range of topics. The study concludes that Industry 5.0, as an evolution of Industry 4.0, is a broad concept that lacks a clear definition, making it difficult to focus on and apply effectively. Therefore, for Industry 5.0 to be useful, it needs to be refined and more clearly defined. Furthermore, the findings demonstrate that well-known AI techniques can be effectively utilized for trend identification, particularly when the available literature is extensive and the subject matter lacks precise boundaries. This study showcases the potential of AI in extracting meaningful insights from large and diverse datasets, even in cases where the thematic structure of the domain is not clearly delineated.
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