Thinking and Research on Ideology Education of University Student Based on Deep Learning in Small Sample Environment

Building an evaluation system for new media ideology education in colleges and other higher education institutions is helpful for assessing the current ideology education and encouraging high levels of information technology integration in ideology education has emerged as a key strategy for this type of education. Based on the central tenet of deep learning theory, ideology education for university students can explore educational strategies from six perspectives in order to achieve deep learning for universities. These six perspectives are opening educational channels, integrating educational contents, assisting knowledge construction, creating educational situations, problem-solving, and developing multiple evaluations. This study proposes a deep learning-based evaluation model for ideology teaching through new media in higher education institutions and colleges, applies deep learning theory to the study's research samples, and calculates the degree of association. Test samples are used to evaluate the network, and positive test outcomes are attained. The deep learning model can effectively increase the accuracy of choosing an ideological and political education approach, as evidenced by its average ideal accuracy of 92.6 percent, which is higher than that of PS-BP and DE-BP, which are 86.4 percent and 82.2 percent, respectively.