| Summary: | The article examines the constraints that limit the learning adaptability of retired re-entry students, beginning with a discussion of their behavioral characteristics. It analyzes the smart learning supported by artificial intelligence technology, constructs a model of the learning adaptability of retired and re-entry students through the smart learning environment, and realizes the personalized learning path recommendation for retired and re-entry students based on the disciplinary knowledge map and KgRank algorithm. To investigate the adaptability of retired and re-study students in higher vocational colleges and universities, an empirical analysis was conducted. The results show that the students scored less than 3 in five aspects: the difficulty of the course, the teacher’s teaching style, the teacher-student relationship, the learning method, and the adaptability to university study. There is no significant difference between the adaptability of students of different genders and academic backgrounds, i.e., P>0.05, which indicates that the problem of adaptability of students returning from military service in higher vocational colleges and universities is more prominent, and colleges and universities must join forces with multiple departments to help students return to military service to adapt to the campus learning life better.
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