Social　link　is　an　important　index　to　understand　master　students＇　mental　health　and　social　ability　in　educational　management.　Extracting　hidden　social　strength　from　students＇　rich　daily　life　behaviors　has　also　become　an　attractive　research　hotspot.　Devices　with　positioning　functions　record　many　students＇　spatiotemporal　behavior　data,　which　can　infer　students＇　social　links.　However,　under　the　guidance　of　school　regulations,　students＇　daily　activities　have　a　certain　regularity　and　periodicity.　Traditional　methods　usually　compare　the　co-occurrence　frequency　of　two　users　to　infer　social　association　but　do　not　consider　the　location-intensive　and　time-sensitive　in　campus　scenes.　Aiming　at　the　campus　environment,　a　Spatiotemporal　Entropy-Based　Analyzing　(S-EBA)　model　for　inferring　students＇　social　strength　is　proposed.　The　model　is　based　on　students＇　multi-source　heterogeneous　behavioral　data　to　calculate　the　frequency　of　co-occurrence　under　the　influence　of　time　intervals.　Then,　the　three　features　of　diversity,　spatiotemporal　hotspot　and　behavior　similarity　are　introduced　to　calculate　social　strength.　Experiments　show　that　our　method　is　superior　to　the　traditional　methods　under　many　evaluating　criteria.　The　inferred　social　strength　is　used　as　the　weight　of　the　edge　to　construct　a　social　network　further　to　analyze　its　important　impact　on　students＇　education　management.