Machine Learning Theory in the Strategic Management of Regional Risk Factors Measurement
Under the background of the state’s strong support for entrepreneurship, domestic small- and medium-sized enterprises ushered in the climax of development, but there are still crises coexisting with opportunities. According to statistics, most small- and medium-sized enterprises cannot survive the f...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Hindawi Limited
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/97edae8b9fdf4e0b853d49f599d911c3 |
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| Sumario: | Under the background of the state’s strong support for entrepreneurship, domestic small- and medium-sized enterprises ushered in the climax of development, but there are still crises coexisting with opportunities. According to statistics, most small- and medium-sized enterprises cannot survive the first three years of the initial stage of entrepreneurship. It can be said that risks exist all the time for enterprises. How to face the risk crisis and effectively avoid these regional risks has become an important factor for enterprises to survive for a long time. The accelerating pace of global economic integration has not only brought opportunities to enterprises but also brought challenges to the survival of enterprises. At present, there are few studies on regional risk in China and most of them are qualitative studies; there is no more specific quantitative study on risk factors. In view of this situation, this paper will study the quantitative evaluation model of regional risk factors based on machine learning. The development of this model adopts the method of support vector machine, which is a more commonly used risk assessment machine learning method. In order to better assess the risk, this paper also establishes a risk assessment index system, which classifies the factors of regional risk in detail and gives the specific evaluation method. Through the combination of modern technologies such as intelligent computing, semisupervised learning, and strategic center organization, the final model is established. After four risk prediction experiments including measuring the net profit margin of total assets of enterprise a, the data shows that the accuracy of the risk assessment model in this paper has been greatly improved compared with the traditional way and shows that the short-term prediction is higher than the long-term prediction and the overall prediction effect is relatively ideal, which can be applied to the practical management of regional risk prediction of enterprises. |
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