Analysis of Learning Ability of Ideological and Political Course Based on BP Neural Network and Improved k -Means Cluster Algorithm

Abstract

With the rapid development of technologies such as big data analysis, machine learning, and cloud computing, artificial intelligence has made breakthrough progress in many fields. Artificial intelligence technology has also brought profound changes to higher education. Therefore, the ideological and political course in colleges and universities should integrate artificial intelligence technology into the teaching of ideological and political education and create an "intelligent ideological and political learning"to adapt to the goal of educational reform in the new era. This paper presents a research method of innovation ability of ideological and political course based on BP neural network and improved k-means clustering algorithm. Firstly, this method obtains the objective index that can comprehensively measure the learning ability through BP neural network and acquires the evaluation score of learning ability. Then, SPSS software is utilized to test the correlation between the influencing factors and the index, harvesting the factors that significantly affect graduate students' ideological and political learning ability. Finally, an improved k-means clustering algorithm is designed, which clusters the graduate students according to the different characteristics of the survey objects and gives targeted suggestions for each class of individuals to improve their ideological and political learning ability. The experimental results indicate that the proposed method is feasible and effective. The research method of ideological and political course ability proposed in this paper is of great significance to the promotion of ideological and political education in the era of big data.