<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper analyzes the three-level inclusion relationship of high-level innovative talents and combs the structure of high-level scientific and technological talent evaluation models based on big data technology. Aiming at the evaluation problems of high-level scientific and technological talents, a fuzzy neural network model is constructed, and at the same time, the R&amp;D middle school effect is utilized to evaluate the innovation achievements of high-level scientific and technological talents. Construct the evaluation index system of high-level scientific and technological innovative talents by utilizing 6 first-level indexes, 14 second-level indexes and 48 third-level indexes. Create a hierarchical analysis structure model, evaluate the indicator data through a judgment matrix and consistency test, and output the indicator weights. Analyze the relevance of the indicator model for different input layer neurons in fuzzy hierarchical analysis through comparative experiments. Use empirical analysis to analyze the innovative evaluation scores of high-level scientific and technological talents in Group A. The experimental results show that when the input layer contains 48 neurons, the loss value ranges from [0.132,1.765], the loss decreases the fastest, the stronger the indicator correlation, the stronger the generalization ability of the fuzzy neural network regression model. The overall scores of the evaluation of high-level scientific and technological talents of Group A for the first and second-level indicators are 3.54 and 3.869, respectively, and the overall view of Group A’s high-level scientific and technological talent innovative ability is better. Good.</p>
</abstract>

This paper analyzes the three-level inclusion relationship of high-level innovative talents and combs the structure of high-level scientific and technological talent evaluation models based on big data technology. Aiming at the evaluation problems of high-level scientific and technological talents, a fuzzy neural network model is constructed, and at the same time, the R&D middle school effect is utilized to evaluate the innovation achievements of high-level scientific and technological talents. Construct the evaluation index system of high-level scientific and technological innovative talents by utilizing 6 first-level indexes, 14 second-level indexes and 48 third-level indexes. Create a hierarchical analysis structure model, evaluate the indicator data through a judgment matrix and consistency test, and output the indicator weights. Analyze the relevance of the indicator model for different input layer neurons in fuzzy hierarchical analysis through comparative experiments. Use empirical analysis to analyze the innovative evaluation scores of high-level scientific and technological talents in Group A. The experimental results show that when the input layer contains 48 neurons, the loss value ranges from [0.132,1.765], the loss decreases the fastest, the stronger the indicator correlation, the stronger the generalization ability of the fuzzy neural network regression model. The overall scores of the evaluation of high-level scientific and technological talents of Group A for the first and second-level indicators are 3.54 and 3.869, respectively, and the overall view of Group A’s high-level scientific and technological talent innovative ability is better. Good.

Research on the construction of evaluation system for high-level scientific and technological talents based on big data analysis

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper analyzes the three-level inclusion relationship of high-level innovative talents and combs the structure of high-level scientific and technological...

Research on the construction of evaluation system for high-level scientific and technological talents based on big data analysis

Pubblicato online: 05 dic 2023

Pagine: -

Ricevuto: 19 gen 2023

Accettato: 15 giu 2023

DOI: https://doi.org/10.2478/amns.2023.2.01361

Parole chiave
Talent evaluation system, Fuzzy hierarchical analysis, Fuzzy neural network, R&D middle school effect, Consistency test

© 2023 Xue Wu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the construction of evaluation system for high-level scientific and technological talents based on big data analysis

Pubblicato online: 05 dic 2023

Pagine: -

Ricevuto: 19 gen 2023

Accettato: 15 giu 2023

DOI: https://doi.org/10.2478/amns.2023.2.01361

Parole chiaveTalent evaluation system, Fuzzy hierarchical analysis, Fuzzy neural network, R&D middle school effect, Consistency test

© 2023 Xue Wu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Talent evaluation system, Fuzzy hierarchical analysis, Fuzzy neural network, R&D middle school effect, Consistency test