Evaluation of College Students’ Educational Quality Based on Integrated Learning Algorithm and Support Vector Machine

With the deepening of education informationization, the importance of a college education quality evaluation system has become increasingly prominent. This paper aims to research and develop a quality evaluation system for college students based on an integrated learning algorithm and support vector machine (SVM). By integrating multiple learning algorithms and the classification ability of SVM, the system comprehensively evaluates students’ learning outcomes. First, we preprocess the educational data, including data cleaning, feature selection and standardization. Then, using integrated learning algorithms, such as random forest and gradient lift tree, the system design adopts a modular architecture, including a data preprocessing module, feature engineering module, model training module and evaluation output module. The experimental results show that the system has high evaluation accuracy and stability on several university education data sets. Compared with the traditional single algorithm, ensemble learning combined with SVM has obvious advantages in dealing with high dimensional and nonlinear educational evaluation problems. In addition, the system also provides a user-friendly interface for educators and students to operate and query. It provides a new technical means for the evaluation of college education quality and also provides data support and decision-making reference for personalized education and precision teaching. In the future, the system will further integrate more machine learning algorithms and explore deeper data analysis techniques to achieve more accurate and comprehensive student quality evaluations.