Yang, Qing and Tang, Youguo (2023) Big Data-based Human Resource Performance Evaluation Model Using Bayesian Network of Deep Learning. Applied Artificial Intelligence, 37 (1). ISSN 0883-9514
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Abstract
Human resource management was of great significance for enterprises to formulate development strategies and improve competitiveness. With the rapid development of new technologies such as knowledge economy and big data, the traditional human resource performance appraisal model had certain limitations and was not objective, which had been difficult to adapt to the increasingly highly integrated development needs of modern enterprises. Therefore, this paper used big data analysis and new technical means to put forward the study of human resource performance evaluation model based on Bayesian network, aiming to provide some theoretical reference for modern enterprise human resource management. First of all, it summarized the related concepts of performance appraisal and its important position in enterprise management, analyzed the characteristics of human resources performance appraisal indicators and their mutual relations, and expounded the common methods and characteristics needed to build a performance appraisal system. Secondly, it summarized the relevant theory of Bayesian network and its advantages in solving complex random problems, and proposed a performance appraisal model based on Bayesian network for the needs of enterprise human resources management. Finally, the balanced scorecard method was used to build a performance appraisal system, and the performance appraisal model proposed in this paper was tested by experiments, and compared with the traditional method. The results showed that the performance appraisal model proposed in this paper had significant advantages and could be better applied to the performance appraisal management of enterprise human resources. The research of this paper can not only provide theoretical guidance for further research on enterprise human resource allocation and management, but also provide reference for performance appraisal in other fields.
Item Type: | Article |
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Subjects: | AP Academic Press > Computer Science |
Depositing User: | Unnamed user with email support@apacademicpress.com |
Date Deposited: | 13 Jun 2023 05:05 |
Last Modified: | 20 Jul 2024 09:19 |
URI: | http://info.openarchivespress.com/id/eprint/1508 |