期刊文献+

数据挖掘与智能筛选视角下的科技期刊选题策划 被引量:12

Topic Selection Planning of Scientific Journals from the Perspective of Data Mining and Intelligent Screening
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摘要 针对科技期刊传统选题策划面临的数据有限、信息精准度不高及以编辑经验为主的问题,探究大数据和人工智能背景下的选题策划优化方法。通过分析大数据和人工智能背景下科技期刊选题策划面临的挑战,结合大数据和人工智能为选题策划优化在数据、算力和算法三个层面提供的理论、方法和技术,探究科技期刊选题策划各关键环节的优化方法。以期运用大数据的智能自动化人机协作功能,科学配置大数据资源,使科技期刊的选题策划决策建立在大数据科学描述、预测和诊断的基础之上,达到科技期刊选题策划的科学化、自动化和精准化。 Aiming at the problems of limited data,low accuracy of information and too much dependence on editors’experience in the traditional topic selection and planning of sci-tech periodicals,this paper probes into the optimization methods of topic selection and planning under the background of big data and artificial intelligence.This paper first analyzes the challenges faced by the topic selection and planning of sci-tech periodicals under the background of big data and the artificial intelligence.Then,it explores the optimization methods for the key parts of topic selection and planning of sci-tech periodicals combining the theoretical methods and technologies provided by big data and artificial intelligence at the level of data,calculation power and algorithm.The goal of optimization of topic selection and planning is expected to be achieved by using the intelligent automatic man-machine collaboration function of big data and scientifically configuring the resources of big data.Based on the scientific description,prediction and diagnosis of big data,the selection and planning decisions of sci-tech periodicals would be more scientific,automatic and precise.
作者 蒋学东 涂鹏 阳丽霞 Jiang Xuedong;Tu Peng;Yang Lixia(Editorial Department of Journal of Railway Science and Engineering,Central South University,Changsha,410075)
出处 《出版科学》 CSSCI 北大核心 2020年第1期36-41,共6页 Publishing Journal
基金 中国科技期刊卓越行动计划项目(卓越计划C-109)部分成果。
关键词 大数据 人工智能 科技期刊 选题策划优化 Big data Artificial intelligence Sci-tech periodical Optimization of topic selection
作者简介 蒋学东,中南大学《铁道科学与工程学报》编辑部副编审;涂鹏,中南大学《铁道科学与工程学报》编辑部编辑;阳丽霞,中南大学《铁道科学与工程学报》编辑部副编审。
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