To　achieve　the　early　perception　and　modeling　of　abnormal　student　behavior,　a　method　of　student　behavioral　temporal　modeling　sensitive　to　abnormal　behavior　for　mental　health　prediction　(SBTM-SABMHP)　was　proposed.　A　heterogeneous　information　network　was　constructed　to　model　students’　current　behavioral　patterns　using　multiple　behavioral　sensing　data　collected　by　mobile　devices　such　as　accelerometers,　sound　sensors,　and　wireless　fidelity　(WI-FI).　Furthermore,　an　attention　mechanism-based　abnormal　behavior-sensitive　gating　module　was　built　for　historical　behavioral　temporal　data　to　effectively　integrate　long-　and　short-term　behaviors　of　students.　In　this　way,　students’　temporal　behaviors　were　modeled,　and　mental　health　status　prediction　was　achieved.　The　comparative　analysis　experiments　of　the　proposed　model　were　conducted　on　the　public　dataset　StudentLife.　Results　show　that　this　method　achieves　the　best　performance　on　all　evaluation　metrics　compared　to　all　other　baseline　methods　for　student　mental　status　prediction,　demonstrating　the　effectiveness　for　student　mental　health　prediction　tasks.　©　2024　Beijing　University　of　Technology.　All　rights　reserved.