Rare earths serve as key raw materials to support high-end technological innovation and the de-velopment of new energy industry.Thus,it is imperative to address the financing difficulties faced by small and medium-sized enterprises(SMEs)in the rare earth supply chain,strengthen China's rare earth indus-try chain,and better safeguard the national strategic interests.As an innovative financing method,supply chain finance has become a major means to realize SME financing credit,but the credit risk issue remains one of the most critical issues that need to be solved in financing decision.Therefore,this paper proposed a hybrid augmented machine learning algorithm.First,the support vector machine(SVM)algorithm was op-timized using the dynamic lens imaging inverse learning improved marine predator algorithm(IMPA),and then the optimized SVM was integrated using the AdaBoost algorithm to build an AdaBoost-IMPA-SVM model.The model was employed to evaluate the financial risk of the supply chain and re-establish the fi-nancial risk system indicators of the supply chain.Then,special effects were selected through correlation analysis,and 140 samples from 52 Chinese listed SMEs in the computer communication and other manufac-turing industries during 2019-2021 were selected and input into the model as characteristic variables.The results of simulation experiments verify that the model has better classification and identification perfor-mance compared with other credit risk evaluation models.
维普
万方数据