Research on Building a New Mode of Civic and Political Education by Image Recognition Technology for Popularizing Socialist Core Values

Abstract

Given the current state of ideological education in colleges and universities, we propose leveraging image recognition technology to foster innovation and digital advancement in this mode of education. The weighted average method is used to greyscale the video images of students’ classrooms, and after binarizing the images using the OTSU algorithm, interference factors are avoided by denoising and angle correction. We combine the motion history image (MHI) and HOG features to create a new feature that depicts Civics classroom behavior. The SVM classifier then performs the classification of classroom behavior, ultimately constructing the Civics classroom behavior recognition model based on HOC+MHII+SVM. This paper’s model and research data are combined to conduct an example analysis of Civics education in colleges and universities. It can be concluded from the analysis that, in the case of simultaneously adopting SVM for recognition and classification, it is found that HOC+MHI feature fusion has a more significant effect on classroom behavior recognition than FPN+Multi-layer CBAM, and the difference of its various indexes is maintained within the range of 0.01~0.08. In addition, the experimental group has a significant difference relationship in the four dimensions of demonstrating the concept of honor and shame, the concept of national development, the value of development, and the value of society, P<0.05. This study provides theoretical references for research on the innovation of the ideological and political education model and is conducive to the promotion of cultivating the formation of students’ socialist core values.