In today’s globalization, English as the common language of international communication, its importance is self-evident. English education in college is a key stage to cultivate students’ language ability and broaden their international vision. However, despite the investment of resources and energy, there are still significant individual differences in the effectiveness of college English teaching, with some students making significant progress and others lagging behind. This encourages educators and researchers to continuously explore more effective teaching methods and evaluation methods. As K-Means data analysis tools, data algorithm and support vector machine (SVM) have powerful classification and clustering capabilities, This research explores the utilization of two specific algorithms to gain deeper insights into the critical factors affecting college English instruction by employing quantitative analysis, thereby offering a robust foundation for enhancing teaching outcomes. According to MATLAB simulations, an evaluation model that combines the K-Means data algorithm with support vector machines proves to be effective in assessing college English instruction when applied under particular assessment standards. This approach yields superior evaluation precision and a more genuine representation of teaching efficacy compared to conventional methods.