High-level talent subject evaluation method based on big data analysis

Abstract

The invention discloses a high-level talent subject evaluation method based on big data analysis. The method comprises the following steps: constructing a high-level talent database; analyzing and processing the high-level talent database to obtain a high-level talent information vector library; setting a corresponding importance weight for each vector dimension of the attribute vector; according to the information of each vector dimension in the attribute vector, screening out a subject set corresponding to the attribute vector, wherein the subject set comprises a plurality of subjects, and each subject in the subject set is marked with a corresponding importance weight; combining all the screened subject sets to obtain a total subject set, and when the subject sets are combined, combining the importance weights corresponding to the same subject in different subject sets as the confidence coefficient of the subject; and screening the total subject set according to the confidence coefficient of each subject. According to the high-level talent subject evaluation method based on big data analysis, the subject direction of high-level talents can be scientifically and reasonably evaluated.

Description

technical field [0001] The invention relates to the field of data processing, in particular to a method for evaluating high-level talent disciplines based on big data analysis. Background technique [0002] Talent is an indispensable part of the current social development process, and it plays a very important role in human civilization and social progress. Among them, the role that high-level talents can play is even greater, and they can improve the construction of human material and spiritual civilization. Therefore, the evaluation of high-level talents is of great significance. [0003] The existing discipline evaluation methods for high-level talents are generally judged according to the discipline in which the papers are published, and the journal judgment method is used to attribute all the papers published by a certain journal to a certain discipline, which is extremely inaccurate. At the same time, the disciplines in which high-level talents publish papers are not...

AI Technical Summary

Problems solved by technology

[0003] The existing subject evaluation methods for high-level talents are generally judged according to the discipline in which the papers are published. The journal judgment method is used, and all articles published in a certain journal are attributed to a certain discipline, which is extremely inaccurate.

At the same time, the disciplines in which high-level talents publish papers are not necessarily consistent with their own disciplines. Dividing them into disciplines by publishing papers will result in inaccurate division of disciplines.

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