The purpose of this chapter is to explore the application of the latent Dirichlet allocation (LDA) model in the field of smart teaching, especially its effectiveness in knowledge topic mining and interdisciplinary research. In response to the complexity of constructing an educational corpus, this chapter first clarifies the scope of data collection, including the target subject area and time frame, and adopts diverse data sources for data collection. A comprehensive and representative corpus is constructed through professional data preprocessing steps, including text cleaning, normalization processing, and stratified sampling. Subsequently, this chapter sets the key parameters of the LDA model and trains the model through Gibbs sampling to explore topics in the teaching corpus. In addition, the optimal number of topics is determined by evaluating the representativeness and relevance of the topics extracted by the model. The research results indicate that the LDA model has significant advantages in topic consistency and stability, effectively capturing potential topic structures in teaching data and generating highly consistent and stable topic models. In addition, the disciplinary intersections revealed by LDA help cultivate students’ comprehensive perspectives and skills. Finally, a series of teaching strategies and curriculum design suggestions are proposed to promote interdisciplinary learning and improve teaching effectiveness while providing new theoretical and practical guidance for the education field.