基于主成分的BP人工神经网络期刊评价——以人文社科期刊为例

Evaluation of BP Artificial Neural Network Journals Based on Principal Components——Take Humanities and Social Science Journals as an Example

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摘要【目的/意义】期刊评价的方法繁多且复杂,无法分辨其中的好坏,对于方法的效果也是难以锚定,使得期刊评价存在一定的模糊性和不确定性。【方法/过程】本文在主成分分析方法的基础上,提出了一种新的期刊评价方法——主成分-BP人工神经网络法,以《中国学术期刊影响因子年报(人文社会科学)》(2021年)的585种综合性人文社科期刊作为评价对象,将评价结果同权威期刊评价结果进行对比,再对评价方法进行分析。【结果/结论】研究结果表明:主成分-BP人工神经网络方法同部分传统方法相比结果更加精准;主成分-BP人工神经网络方法对评价对象要求较高;为其他领域期刊评价以及评价方法提供一定的借鉴思路。【创新/局限】本文仅以人文社科期刊为例,范围有一定的局限性,今后应进一步扩大研究主体范围并尝试将这种方法用于其它领域的评价。【Purpose/significance】There are many and complex methods for journal evaluation,and it is impossible to distinguish between good and bad,and it is difficult to anchor the results of the methods,which makes journal evaluation ambiguous and inaccurate.【Method/process】In this paper,based on the principal component analysis method,a new journal evaluation method,the principal component-BP artificial neural network method,is proposed to evaluate 585 comprehensive humanities and social science journals in the Annual Report on Impact Factor of Chinese Academic Journals(Humanities and Social Sciences)(2021),and the evaluation results are compared with authoritative journal The evaluation results are compared with those of authoritative journals,and then the evaluation method is analyzed.【Result/conclusion】The research results show that:the principal component-BP artificial neural network method is more accurate than some traditional methods;the principal component-BP artificial neural network method has higher requirements for evaluation objects;meanwhile,To provide some ideas for other fields of journal evaluation as well as evaluation methods.【Innovation/limitation】This paper only takes humanities and social science journals as an example,and the scope is limited.In the future,we should further expand the scope of research subjects and try to apply this method to evaluation in other fields.

作者韩雷邱均平 HAN lei;QIU Jun-ping(School of Economics and Management,Zhejiang Sci-Tech University,Hangzhou 310018,China;Chinese Academy of Science and Education Evaluation,Hangzhou Dianzi University,Hangzhou 310018,China)

机构地区浙江理工大学经济管理学院杭州电子科技大学中国科教评价研究院

出处《情报科学》 CSSCI 北大核心 2022年第10期107-113,共7页 Information Science

基金国家社会科学基金重大项目“基于大数据的科教评价信息云平台构建和智能服务研究”(19ZDA348)

关键词主成分分析 BP人工神经网络评价指标人文社科期刊评价 principal component analysis BP artificial neural network evaluation index humanities and social sciences journal evaluation

分类号 G237.5 [文化科学]

作者简介韩雷(1979-),女,黑龙江哈尔滨人,博士,主要从事科学评价、信息计量、大数据挖掘、金融计量研究;邱均平(1947-),男,湖南涟源人,资深教授,博士生导师,主要从事信息计量与科教评价研究。