In view of problems such as discreteness and sparsity in the massive data accumulated by “campus big data”, how to detect potential students with abnormal behavior from the campus student groups with a large base, wide activity ranges, and strong personality has become an urgent issue to be solved in the analysis of abnormal behavior of students. This study proposes an early warning method for abnormal behavior of college students based on multi-modal fusion in big data environment (EWMAB). First of all, in view of the insufficient representation of student behavior portraits and the timeliness and dynamics of behavior labels, a cross-modal student behavior portrait model based on multi-modal feature deep learning is established; secondly, for the timeliness and post-alarm of the prediction and early warning of abnormal behavior of students, a multi-modal fusion-based early warning method for student abnormal behaviors is proposed based on the student behavior portrait and student behavior classification prediction. Through the long and short term memory network (LSTM), combined with student behavior multi-index data and text information, the problem of early warning of students’ abnormal behaviors is solved; finally, this study uses an example to verify the model and takes the early warning of abnormal academic performance of students as an example. Compared with other early warning algorithms, the EWMAB method can improve the accuracy of early warning and realize the timeliness and pre-alarm of abnormal behaviors of students so that the education of students is more targeted, personalized, and predictable.