Abstract
The local government bond system of China has experienced a series of changes from its initial creation to its abolition and then to a recovery again. During the period, the central government always dominated the changing direction of the local government bond system. However, as fiscal decentralization reform has progressed, the institutional needs of local governments and investors have gradually gained attention. As a result, the size and variety of local government bonds are expanding. Through the introduction of analysis of system change based on situation structure performance (SSP), this paper uses Machine Learning (ML) approaches to predict the risk of government debt of China in the context of changing the local government bond system. Besides, this research work includes the comprehensive weight assignment for government debt hazard, fiscal revenue forecasting, default risk calculation, and finally an analysis of the validity of government debt hazard. The system may provide financial signal advice and strategy reference for dealing with hazards in early payment, organizing debt repayment significance order, optimizing fiscal revenue and cost structure, and so on.
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