Evaluation and Analysis of Comprehensive Influence of Papers: Multidisciplinary as an Example

Abstract

By introducing supplementary evaluation indexes, this paper makes up for the deficiencies of lag, injustice, discipline bias, and one-sidedness of traditional citation evaluation. Multidisciplinary papers are selected as the data source. Correlation analysis, validity analysis, factor analysis, and principal component analysis are used to analyze the data of each index to construct a comprehensive influence evaluation model. The results show that the model is a comprehensive evaluation model with academic evaluation as the main and social evaluation as the auxiliary. The comprehensive influence score of papers can be calculated through comprehensive influence formulas, to obtain a more comprehensive and reasonable evaluation result. This paper provides data support for the proportion of each index data in the comprehensive evaluation of academic papers, and also provides a reference for index selection and evaluation model optimization of comprehensive influence evaluation of papers.