Construction of University Political Teaching System Based on Probabilistic Neural Network Model

Abstract

The development of computer technology has made intelligence a way to solve problems in many industries. There are some problems in political teaching in colleges and universities, and colleges and universities urgently need to build an ecological political teaching system, and the conceptual neuron network model can be a means of realization. A conceptual neuron network is a branch of artificial intelligence technology that can help the system perform accurate discriminant analysis. This article takes the current situation of ideological and political education in colleges and universities as the research background, and tries to establish an artificial intelligence network research learning platform based on the depth of college political culture. This article introduces the probabilistic neural network, and uses the neural network to build a system that can reflect the defects of school politics teaching and propose solutions to the defects. Finally, it is verified by MATLAB that the artificial intelligence prediction based on the probabilistic neural network has good convergence and fault tolerance. And data processing capabilities. The construction of this system allows universities to clearly observe the parts of political education that need to be strengthened through data, which is conducive to the improvement of political education.