Convolutional neural network and AI technology for colleges and universities students' achievement performance prediction.

With the growing demand for Educational Data Mining (EDM), addressing challenges in educational practice through advanced algorithms remains complex. Key issues include the heterogeneity of educational data, the difficulty in quantifying students' cognitive and behavioral traits, the absence of robust multimodal data fusion strategies, and the limited generalizability of traditional models in small-sample contexts. This study investigates the application of Artificial Intelligence (AI), specifically Convolutional Neural Network (CNN), in predicting student academic performance in colleges and universities to optimize resource allocation and improve learning outcomes. The dataset incorporates students' demographic information, academic records, and learning behavior, with comprehensive preprocessing to ensure data integrity. Experimental results indicate that the proposed model achieves a prediction accuracy of 90.7%, precision of 86.4%, recall of 85.2%, and an F1 score of 86.1%. For a test group of 500 students, the model predicts an average score of 82.5 and a median score of 83. These findings demonstrate the feasibility of integrating AI into educational systems for accurate performance forecasting and personalized learning, offering a strong foundation for future research and practical implementation.