Research on Classification and Popularization of Folk Drama Based on Data Mining and SVM

This study puts forward a classification method of operas based on SVM, which combines with art education in primary and secondary schools to spread folk art. The accuracy of learning classification by support vector machine is 87.5%. Compared with other algorithms, SVM has 11.36% higher accuracy and 1.24% higher accuracy than the Naive Bayesian algorithm. The specificity of support vector machines is 0.1584 higher than that of the Naive Bayesian and 0.0426 higher than that of logistic regression. The experimental results show that the SVM algorithm has a good classification effect on the learning of the play.