## 1 Introduction
[17] leveraged AutoML to enhance the accuracy of predicting student performance using available data upon entering academic courses. [PERSON] et al. [18] designed a predictive model for student academic performance based on statistical feature extraction, evaluating the model's performance using classifiers such as random forests and support vector machines. [PERSON] et al. [19] proposed a behavior-based electronic learning performance prediction framework (BCEP), which improves prediction accuracy through four steps--data cleaning, behavior classification, feature fusion, and model training--and introduced the PBC model to classify e-learning behaviors, thereby enhancing both learning performance prediction effectiveness and model applicability. It is evident that extensive research has been conducted on predicting students' academic performance using machine learning technology. However, there is still a lack of research on the correlation of course performance, which is crucial for guiding the course arrangement in computer-related majors at universities. Moreover, due to the varying abilities of different machine learning algorithms in processing different types of data and the varying accuracy of training on different datasets, a single machine learning model may not effectively function in real-world teaching scenarios. To address these issues, this study focuses on practical application scenarios in student education management. It establishes a machine learning framework based on multi-model integration to improve the accuracy of student performance prediction for undergraduate majors. The study begins by conducting a correlation analysis of the scores in 17 specialized courses selected from the student training program. It discovers that these courses can be grouped into four collections, each exhibiting strong positive correlations. Next, multiple machine learning models are trained on this data to complete regression tasks for predicting student performance or classification tasks for predicting student performance ratings. It is observed that different machine learning models exhibit varying abilities to predict accurately for different courses or tasks. Finally, to construct a robust and reliable student performance prediction system, multiple machine learning models are integrated, and the optimal model is selected using accuracy voting. This leads to the development of a well-generalized student performance prediction system. The main contributions of this study can be summarized as follows:  
1. Course Correlation Analysis: The study conducts correlation and cluster analyses on 17 specialized courses in the computer major training program. The identification of four sets of highly related courses provides valuable insights for optimizing the teaching arrangement of computer curriculum systems in universities. 2. Evaluation of Multiple Machine Learning Algorithms: Through numerous experiments, the study validates the performance of various machine learning algorithms in predicting student performance under different tasks and datasets. The findings highlight the inconsistency in the performance of different models and emphasize the limitations of relying on a single machine learning method for accurate predictions in real-world teaching scenarios. 3. Multi-Model Fusion Framework: To address the limitations of a single machine learning model, this study proposes a multi-model fusion framework based on machine learning. The framework utilizes accuracy voting to select the optimal model for each dataset or task, resulting in a more reliable and comprehensive student performance prediction system. This framework offers valuable insights and guidance for university administrators in conducting academic prediction and early warning.