The current educational landscape is undergoing subtle changes, and smart classrooms urgently require upgrades and innovations. This study takes School A, which is undergoing educational reforms, as an example. First, using a weekly time unit, we collected a series of data on students’ consumption behavior (including consumption frequency, consumption time, and consumption amount), online behavior (including online frequency and online time), and daily routines (including start time of activities and preparation time for rest) over two semesters, thereby constructing a multi-source dataset of students’ daily behaviors. The data is preprocessed and corresponding features are extracted. Then, a multi-feature combination-based online course performance prediction model is proposed. Finally, a student academic performance management system based on multi-source information fusion technology is constructed. Through experiments, the accuracy rate of the student performance prediction model based on feature combinations can reach over 90%. The student academic performance management system constructed based on this model is accepted by most students and can significantly improve student performance compared to traditional models. Additionally, applying multi-source information fusion technology to teaching, learning, and management processes can promote the joint development of schools and students.