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

Abstract

Education refers to ideologies, traditions, culture, and values that guide education to economics, politics, morals, religions, information, reality, comparative and historical aesthetic, and artistic school knowledge. The challenging characteristics in political education include lack in knowledge sharing, user’s interactive experience, and incentive mechanism has become an essential factor. In this paper, the Deep Learning-Based Innovative Ideological Behavior Education Model (DL-IIBEM ) has been proposed to strengthen the mechanism to promote information exchange, enhance the user’s interactive experience, and make the platform perform efficiently. Knowledge Network Mechanism Analysis is integrated with DL-IIBEM to strengthen user feedback probability, the average probability of completing social media tasks on a popular network, and the predicted utility degree for individual users. The entire platform is dramatically improved. The simulation analysis is performed based on the performance ratio based on data set 1 (98.2%) and 2 (95.3%), skill development ratio (95.3%), accuracy ratio, the teaching methods in ideological and political education, and Students Achievements ratio (98.2%) prove the proposed framework’s reliability.