The traditional academic warning methods for students in higher vocational colleges are relatively backward, single, and have many influencing factors, which have a limited effect on improving their learning ability. A data set was established by collecting academic warning data of students in a certain university. The importance of the school, major, grade, and warning level for the students was analyzed using the Pearson correlation coefficient, random forest variable importance, and permutation importance. It was found that the characteristic of the major has a great impact on the academic warning level. Countermeasures such as dynamic adjustment of majors, reform of cognitive adaptation of courses, full-cycle academic support, and data-driven precise intervention were proposed to provide theoretical support and practical paths for universities to improve the efficiency of academic warning and enhance students’ learning ability.