Abstract:

[Purposes] This paper aims to elucidate the recent advancements in intelligent research pertaining to text and data mining in scientific journals in China and abroad and explore strategic solutions for the intelligent development of text and data mining in scientific journals. [Methods] We conducted literature retrieval in English databases, including Web of Science (SCIE, SSCI, and ESI), ScienceDirect, Elsevier, as well as Chinese databases such as CNKI from 2019 to 2023 to investigate the progress on intelligent research in text and data mining in scientific journals in China and abroad. Moreover, we explored the intelligent development directions of text and data mining in scientific journals and provided suggestions for strategic solutions in this field. [Findings] At present, the current state of intelligent research on text and data mining in scientific journals abroad reveals significant progress in various areas, including topic planning, preprints, literature evaluation, peer review, as well as model exploration and methodologies. However, Chinese scientific journals face challenges such as insufficient technology integration capability, inadequate publishing practices, imprecise text data extraction, and incomplete intelligent algorithms and logic. Therefore, it is necessary to strengthen the basic layout of text and data mining, promote integrated development, encourage research on publishing practices, conduct in-depth mining of text and data, and further build complete text and data mining rules for scientific journals. [Conclusions] The intelligent development of text and data mining in scientific journals can expedite and enhance researchers' access to abundant scientific literature information, providing deeper insights and research directions. However, further research and practical exploration are still needed to develop technical application solutions in this field.

Key words: Text and data mining, Text mining, Data mining, Scientific journal, Artificial intelligence

摘要：

【目的】 对国内外科技期刊文本与数据挖掘智能化研究进展进行阐述,为科技期刊文本与数据挖掘智能化发展探寻对策方案。【方法】 采用文献分析法,检索2019—2023年英文数据库Web of Science(SCIE、SSCI、ESI)、ScienceDirect、Elsevier及中国知网数据库中的科技期刊文本与数据挖掘智能化研究,梳理科技期刊文本与数据挖掘智能化的不同方向,并为科技期刊文本与数据挖掘智能化发展提供对策建议。【结果】 目前国外科技期刊文本与数据挖掘智能化研究在选题策划、预印本、文献评估、同行评议以及模型探索和方法学等方面取得一定进展,我国中文科技期刊在文本与数据挖掘的智能化方面尚存在技术融合能力不足、出版实践不足、文本数据抓取不精准、智能算法和逻辑不完善等问题。应加强文本与数据挖掘基础布局,促进融合发展;鼓励出版实践研究,开展文本与数据深度挖掘;进一步构建完整的科技期刊文本与数据挖掘规则等。【结论】 科技期刊文本与数据挖掘智能化发展可帮助科研工作者更快捷、更准确地获取大量的科技文献信息,为科研工作者提供更深入的思考和研究方向,但其技术应用方案仍需进一步研究探索。

关键词: 文本与数据挖掘, 文本挖掘, 数据挖掘, 科技期刊, 人工智能