Teaching Quality Analysis of College Ideological and Political Education Based on Deep Learning

Abstract

A social activity that uses certain ideas, concepts, political views, and moral values in a society or social group enriches students’ ideology and allows learners to form ideological and moral qualities that correspond to their social and political establishment. The continuous improvement of their complete quality and technical skills is at the heart of social and economic growth. In ideological and political education, risk factors are widely influenced, including the impact of educational purposes and education providers. In this paper, Deep Learning-Based Innovation Path Optimization Methodology (DL-IPOM) has been proposed to strengthen data awareness, improve the way of thinking in ideological and political education. The political instructional collaborative analysis is integrated with DL-IPOM to boost Ideological and political education excellence. The simulation analysis is conducted at (98.22%). The consistency of the proposed framework is demonstrated by efficiency, high accuracy (98.34%), overshoot index rate (94.2%), political thinking rate (93.6%), knowledge retention rate (80.2%), reliability rate (97.6%), performance (94.37%) when compared to other methods.