Semantic Feature Analysis of Intelligent Business Teaching System Based on Deep Learning

This study aimed to enhance students' language proficiency by exploring a Bayesian personalized business English learning method based on deep learning. First, the study improved English vocabulary instruction through semantic feature analysis. The experiment used deep learning models to extract features from business English teaching data, which were then input as observational data into Bayesian models. These models integrated prior knowledge and observational data to derive posterior probability distributions, which assessed student learning states, predicted future performance, and optimized teaching strategies. Second, by offering comprehensive recommendations and relevant indices, the system provided users with content suggestions aligned with their learning needs, expediting the learning process. Finally, the practical application of the proposed method was validated through empirical case analysis. By analyzing student learning outcomes and performances, the system could formulate personalized learning plans and instructional strategies, achieving on-demand learning and precision teaching.