Application of PCA-SVM Model in Financial Crisis Early Warning System of Listed Manufacturing Companies

Abstract

Manufacturing industry has always occupied this very important proportion in the national economy. However, with the economic problems in the past two years, some enterprises began to have a deficit crisis. In order to fundamentally alleviate and solve the financial problems of enterprises, this paper studies the financial model prediction of enterprises based on principal component analysis and support vector machine. The multiple financial analysis trajectories are constructed by using PCA-SVM model, and the results are compared with those of logistic model, BP neural network model, and single support vector machine. Experiments show that the prediction level of PCA-SVM model is the best, and the accuracy of the second model is not as good as this model. The error rates of the second type are 14.81%, 14.54%, and 7.67%, respectively, which are higher than those of PCA-SVM model. After comparing the models in this paper, it is found that the components of the model should be extracted first, and then the data operation should be carried out. Such calculation trajectory has a high level of accuracy. The research of this paper provides reference value for solving the financial problems of manufacturing enterprises.