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Academic Journal of Business & Management, 2022, 4(16); doi: 10.25236/AJBM.2022.041616.

Analysis of Economic Risk Identification Evaluation and Management and Control Strategy Based on Support Vector Machine Algorithm


Jingyang Cui1, Haixin Huang2

Corresponding Author:
Jingyang Cui

1Shaanxi Branch Sales Department, Agricultural Development Bank of China, Xi'an, Shaanxi, 710061, China

2Faculty of Business, Economics & Law, The University of Queensland, Brisbane, Queensland, 4072, Australia


With the deepening of economic globalization, China's participation in global foreign trade activities is constantly diversified, and at the same time it faces more and more risks. China's long-term accumulated risks in the future may be released intensively, causing high incidence. Potential risks in macro-economy, business environment, sovereign credit, debt and other fields will bring certain losses to enterprises and even hinder their development. Support Vector Machine (SVM) based on statistical learning theory is a new machine learning algorithm, which can successfully deal with classification and regression problems. Because of the excellent learning performance of SVM, this technology has become a research hotspot in current academic circles. This paper expounds the basic theory of support vector machine in detail, constructs the basic framework of economic investment risk prediction model based on support vector machine, and gives the concrete steps to realize the model and the key problems to be solved. The algorithm is very practical. Using support vector machine regression theory to predict economic risk not only enriches the existing risk prediction methods in theory, but also has important value in practical application.


support vector machine algorithm; economic risk; risk assessment and control

Cite This Paper

Jingyang Cui, Haixin Huang. Analysis of Economic Risk Identification Evaluation and Management and Control Strategy Based on Support Vector Machine Algorithm. Academic Journal of Business & Management (2022) Vol. 4, Issue 16: 95-98. https://doi.org/10.25236/AJBM.2022.041616.


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