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<p>Analyzing the economic benefits of enterprise employees is better for protecting the rights and interests of enterprise employees. In this paper, based on the K-means clustering algorithm in the context of the Internet, the K-means clustering algorithm is optimized by the entropy weight method, and a K-means clustering model for exploring the analysis of the economic benefits of enterprise employees’ labor is proposed. For the model of this paper, the classification accuracy of the model is verified by employing controlled experiments, and two types of evaluation indexes for the labor economic efficiency of enterprise employees are analyzed and mined, namely labor employment and work environment and wage compensation and training and learning, using enterprise T as an example. Regarding labor and work environment indicators, 74.52% of the evaluations were above C grade. From the data of the indicators of salary and compensation and training and learning, the percentage of C grades or above is 89.41%. In the background of the Internet, the K-means algorithm model to analyze the relationship between the economic benefits of enterprise employee labor can help enterprises understand the current problems of employee labor and improve the happiness of employee labor, thus promoting the increase of enterprise economic benefits.</p>
</abstract>

Analyzing the economic benefits of enterprise employees is better for protecting the rights and interests of enterprise employees. In this paper, based on the K-means clustering algorithm in the context of the Internet, the K-means clustering algorithm is optimized by the entropy weight method, and a K-means clustering model for exploring the analysis of the economic benefits of enterprise employees’ labor is proposed. For the model of this paper, the classification accuracy of the model is verified by employing controlled experiments, and two types of evaluation indexes for the labor economic efficiency of enterprise employees are analyzed and mined, namely labor employment and work environment and wage compensation and training and learning, using enterprise T as an example. Regarding labor and work environment indicators, 74.52% of the evaluations were above C grade. From the data of the indicators of salary and compensation and training and learning, the percentage of C grades or above is 89.41%. In the background of the Internet, the K-means algorithm model to analyze the relationship between the economic benefits of enterprise employee labor can help enterprises understand the current problems of employee labor and improve the happiness of employee labor, thus promoting the increase of enterprise economic benefits.

Analysis and research on the economic benefits of employee labor in enterprises in the context of the Internet

Applied Mathematics and Nonlinear Sciences

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

Analyzing the economic benefits of enterprise employees is better for protecting the rights and interests of enterprise employees. In this paper, based on the...

Analysis and research on the economic benefits of employee labor in enterprises in the context of the Internet

Publié en ligne: 28 avr. 2023

Pages: -

Reçu: 01 mai 2022

Accepté: 28 sept. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00111

Mots clés
Internet, K-means, Entropy method, Employee labor, Economic efficiency

© 2023 Hong Wang, published by Sciendo

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

Analysis and research on the economic benefits of employee labor in enterprises in the context of the Internet

Publié en ligne: 28 avr. 2023

Pages: -

Reçu: 01 mai 2022

Accepté: 28 sept. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00111

Mots clésInternet, K-means, Entropy method, Employee labor, Economic efficiency

© 2023 Hong Wang, published by Sciendo

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

Mots clés
Internet, K-means, Entropy method, Employee labor, Economic efficiency