The role of predictive analysis in identifying high-risk students and implementing early intervention strategies

China's science and education industry is advancing rapidly, with an increasing number of college students. The problems of low comprehensive quality and insufficient abilities of college graduates are becoming increasingly prominent. Learning analysis is based on data mining (DM) technology, which collects and analyzes a large amount of learning data generated by learners during the learning process, discovers the learning characteristics and problems of learners, and enables teachers to adopt reasonable intervention measures to improve their learning effectiveness. Early warning is an important theme in online learning, and identifying students at academic risk through early warning can help teachers carry out personalized teaching interventions in a timely manner. This article proposes a student learning risk prediction method based on deep learning (DL) to address the issues of academic risk and increased difficulty in university management among college students. By analyzing student learning data, it is possible to predict their academic performance and future academic growth in advance, identify students at potential risk, and provide corresponding attention and assistance. The experimental results show that the method proposed in this article can effectively identify high-risk students, and targeted intervention measures can help students avoid academic difficulties, improve their learning motivation and grades.