Classification of College Students with Financial Difficulties Based on Combination Kernel Function

In order to further improve the accuracy of college financial aid, a support vector machine (SVM) classification model for college students with financial difficulties based on combined kernel function was constructed. According to the consumption data, personnel information and historical funding information of the students in school, sample features are extracted. Using the local fitting ability of radial basis function (RBF) kernel and the generalization ability of polynomial kernel, a SVM classification model based on RBF kernel and polynomial kernel was constructed; The multi-grid search method was used to train the model to obtain the optimal kernel parameters and the weight coefficients of the combined kernel function, and to classify and predict the students with financial difficulties. The experimental results show that the constructed model can be used to predict the financial difficulties of college students, and compared with single kernel SVM, logistic regression model and nearest neighbor algorithm (KNN), the accuracy of classification is significantly improved; Using fusion features can increase the difference of different types of sample data, and help to improve the accuracy of classification.