Research on the Early Warning of Enterprise Financial Risks Based on Random Forest
WU Meigui
Abstract：Due to the lack of judgment methods on the risk level of financial data in the process of enterprise financial risk early warning,the accuracy of enterprise financial risk early warning is low and the early warning effect is poor.Therefore,this paper studies the enterprise financial risk early warning based on random forest.By constructing an enterprise financial data recognition function,the enterprise financial data can be recognized.After eliminating data noise by calculating the pheromone concentration of the financial data,the features of the enterprise financial data can be extracted.By calculating the feature threshold,the risk level of the financial data can be determined.Once the risk level of the financial data is determined,the category of the enterprise financial data can be calculated.Then,the k-means algorithm is used to iterate the clustering centers of the enterprise financial risks and classify the enterprise financial risks.According to the classification results,combined with the random forest algorithm,an early warning model is constructed for the enterprise financial risks to achieve the early warning of the enterprise financial risks.The experimental results show that when the number of decision trees is 5 and the maximum depth is 7,the designed system can improve the accuracy of enterprise financial risk early warning.
Keywords：random forest algorithmfeature extractionrisk early warningfinancial risksdecision treemembership degree
Publication Date：2025-03-31
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 72-77 )