Applied Mathematics and Nonlinear Sciences
Journal license

Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 9, Issue 1


Published
on

February 26, 2024


Pages


DOI

Article

An innovative approach to corporate HR training based on deep learning

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Authors

Xiaoxu Chen Affiliation:
Personnel Department, Jiangsu College of Finance & Accounting, Lianyungang, Jiangsu, 222061, China.
, Hanyang Chen Affiliation:
School of Information Engineering, Jiangsu College of Finance & Accounting, Lianyungang, Jiangsu, 222061, China.
and Jinhua Xu Affiliation:
Student Affairs Division, Nanjing, Jiangsu, 211100, China.


Abstract

This article constructs an enterprise human resource training system based on the DACUM method, aiming to solve the critical problems in enterprise training. The article improves the training effect by optimizing the training process, establishing an effective assessment mechanism, and perfecting the feedback and incentive mechanism. Aiming at the limitations of the DACUM method in training guidance, the article introduces a rough support vector machine to categorize the dimensions of competency factors. It constructs an employee effectiveness evaluation model by calculating the weights. Combined with data mining techniques, it provides decision support for managers. The results show that the optimized training system significantly improves employees’ innovative behavior, psychological capital and job performance, in which the regression coefficients of creative behavior and psychological capital are 0.593 and 0.404, respectively. The degree of explanation of job performance in the three dimensions of task, relationship, and dedication is 18.8%, 32.7%, and 30.9%, respectively, which verifies the effectiveness and practical value of the training system.


Keywords

DACUM method, Rough support vector machine, Data mining, competency base, Human resource training, 68M11


Citation

Chen, X., Chen, H., & Xu, J. (2024). An innovative approach to corporate HR training based on deep learning. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns-2024-0419

Published by: Engineering Journals

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