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<p>In the background of the era of digitalization of data information, artificial intelligence algorithms are becoming increasingly intelligent, and the traditional enterprise legal risk assessment fits well with the requirements of the development of the era of big data. This paper focuses on the legal risk assessment of enterprise labor dispatch employment under the clustering algorithm, firstly, by reviewing the literature to understand the concept and principle of the clustering algorithm, constructing the legal risk assessment model of enterprise labor dispatch employment under the clustering algorithm, and applying The legal risk of enterprise labor contract management was assessed by expert scoring, and the risk assessment result was 62.2 points, which was a high score and its legal risk level was high and needed to be improved. The Gaussian mixture model uses probability distribution to determine the degree of similarity between samples, and the three indicators that contribute most to the effect of Gaussian mixture clustering are selected. The purpose is to identify more high-risk enterprises outside the framework of the “special treatment” system and to provide guidance on measures to prevent the legal risks of labor dispatch. Therefore, the legal risk assessment of labor dispatching in enterprises under the clustering algorithm has certain implications for business operators and academics who study the legal risk assessment of labor dispatching.</p>
</abstract>

In the background of the era of digitalization of data information, artificial intelligence algorithms are becoming increasingly intelligent, and the traditional enterprise legal risk assessment fits well with the requirements of the development of the era of big data. This paper focuses on the legal risk assessment of enterprise labor dispatch employment under the clustering algorithm, firstly, by reviewing the literature to understand the concept and principle of the clustering algorithm, constructing the legal risk assessment model of enterprise labor dispatch employment under the clustering algorithm, and applying The legal risk of enterprise labor contract management was assessed by expert scoring, and the risk assessment result was 62.2 points, which was a high score and its legal risk level was high and needed to be improved. The Gaussian mixture model uses probability distribution to determine the degree of similarity between samples, and the three indicators that contribute most to the effect of Gaussian mixture clustering are selected. The purpose is to identify more high-risk enterprises outside the framework of the “special treatment” system and to provide guidance on measures to prevent the legal risks of labor dispatch. Therefore, the legal risk assessment of labor dispatching in enterprises under the clustering algorithm has certain implications for business operators and academics who study the legal risk assessment of labor dispatching.

Legal risk assessment of enterprise labor dispatch employment under clustering algorithm

Applied Mathematics and Nonlinear Sciences

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

In the background of the era of digitalization of data information, artificial intelligence algorithms are becoming increasingly intelligent, and the...

Legal risk assessment of enterprise labor dispatch employment under clustering algorithm

Data publikacji: 17 lip 2023

Zakres stron: -

Otrzymano: 11 mar 2022

Przyjęty: 19 lis 2022

DOI: https://doi.org/10.2478/amns.2023.2.00054

Słowa kluczowe
Big data, Enterprise labor dispatch, Clustering algorithm, Evaluation model, Legal risk

© 2023 Weixia Wang, published by Sciendo

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

Legal risk assessment of enterprise labor dispatch employment under clustering algorithm

Data publikacji: 17 lip 2023

Zakres stron: -

Otrzymano: 11 mar 2022

Przyjęty: 19 lis 2022

DOI: https://doi.org/10.2478/amns.2023.2.00054

Słowa kluczoweBig data, Enterprise labor dispatch, Clustering algorithm, Evaluation model, Legal risk

© 2023 Weixia Wang, published by Sciendo

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

Słowa kluczowe
Big data, Enterprise labor dispatch, Clustering algorithm, Evaluation model, Legal risk