In recent years, with the widespread application of machine learning technology in the field of education, this study is dedicated to assessing the impact of multidimensional data on students’ academic performance in Business English courses. By collecting six key types of data, including teaching evaluations and classroom performance, this paper utilizes the Support Vector Machine (SVM) model and takes students’ final grades as the prediction label for in-depth analysis. In addition, we conducted comparative experiments with models such as Random Forest and K-Nearest Neighbors (KNN) to validate the superiority of the SVM model. Specifically, through ablation studies, this research individually examined the contribution of different features to the model's prediction accuracy, finding that classroom performance and learning outcomes have the most significant impact on accurately predicting students’ final grades, while the influence of study habits and learning preferences is relatively minor. The SVM model performed best across all testing metrics, achieving an accuracy rate of 96.64%, which fully demonstrates its application value in the evaluation of Business English courses. These findings are significant for optimizing teaching methods and advancing personalized teaching, offering valuable references for future related research.