A Classification Method of Network Ideological and Political Resources Using Improved SVM Algorithm

In order to improve the accuracy and efficiency of the classification of network ideological and political resources and promote the efficiency of ideological education, a research on the classification of network ideological and political resources based on the improved SVM algorithm is proposed. We analyze the characteristics and current situation of network ideological and political resources and conclude that the method elements are open and technical. The ontology elements are rich and shared, and the behavioral elements are autonomous and interactive. Three types of network ideological and political resources are proposed: the main resource, content resource, and means resource. The particle swarm algorithm is used to improve the SVM algorithm. In the process of constructing the SVM classifier, the fuzzy membership function is introduced, the classification problem of network ideological and political resources is converted into a secondary planning problem, and the accuracy of network ideological and political resources is finally realized. Simulation results show that the use of improved algorithms to classify network ideological and political resources can improve the accuracy and efficiency of network abnormal data classification.