Strategies for Ideological and Political Education in Colleges and Universities Based on Deep Learning

Ideological and political education in colleges and universities is routinely burdened with the job of building morality and cultivating people, which is related to the cultivation of college students' ideals and beliefs, spiritual pursuits, and political literacy. Based on self-determination theory (SDT), this paper modeled different learning motivations in the early stage of ideological and political courses and analyzed the learning motivation of different student groups combining the Gaussian mixture model (GMM) and stacked autoencoder (SAE). Meanwhile, the study in this paper compared the participation characteristics of different learning motivation clusters, the differences between the ideological and political course performances of students with different learning motivations, and the potential link between learning motivation and learners' educational level. The experimental results show that students with extrinsic motivation will have better performance in the courses. The strength of extrinsic motivation is positively correlated with students' academic performance, and 70% of students with intrinsic motivation achieve excellent results. In addition, the χ ² test result of the two courses selected is 6.442, which confirms the effectiveness of the clustering model proposed in this paper from the side and provides effective theoretical support for the implementation and reform of ideological and political education strategies.