Research on Comprehensive Academic Level Evaluation Indicators and Models for Scientific and Technological Talents Based on Machine Learning
[Objective/Significance]This study breaks the limitations of a single indicator from four dimensions:academic integrity,research output,research activities,and academic reputation,providing new ideas and references for breaking the"4 Single dimension"and"setting new standards"in talent evaluation.[Methods/Processes]For the characteristics of scientific and technological talents,98 quantitative indicators and qualitative characteristic indicators were constructed.This study selected research samples for dataset construction and data preprocessing to form the input data for the final recognition and prediction model;Obtain the implicit relationship between sample feature indicators and evaluation results through machine learning,compare the academic performance of talents under 9 model algorithms,comprehensively determine the optimal model for subsequent training and optimization,and evaluate the model by comparing the model output with actual results.[Limitations]This research model is only based on experimental sample data size and features,and further training and optimization of sample data are needed for its widespread application.[Results/Conclusions]Empirical research has shown that the model has good applicability for the evaluation of scientific and technological talents,providing new perspectives and methods for the evaluation of scientific and technological talents.
Scientific and Technological TalentsTalent EvaluationTalent IdentificationEvaluation Models
NETL
NSTL
万方数据
