Students　spend　most　of　their　time　living　and　studying　on　campus,　especially　in　Asia,　and　they　form　various　types　of　associations　in　addition　to　those　with　classmates　and　roommates.　It　is　necessary　for　university　authorities　to　master　these　types　of　associations,　so　as　to　provide　appropriate　services,　such　as　psychological　guidance　and　academic　advice.　With　the　rapid　development　of　the　＂smart　campus,　＂　many　kinds　of　student　behavior　data　are　recorded,　which　provides　an　unprecedented　opportunity　to　deeply　analyze　students＇　associations.　In　this　paper,　we　propose　a　visual　analytic　method　to　construct　students＇　association　networks　by　computing　the　similarity　of　their　behavior　data.　We　discover　student　communities　using　the　popular　Louvain　(or　BGLL)　algorithm,　which　can　extract　community　structures　based　on　modularity　optimization.　Using　various　visualization　charts,　we　visualized　associations　among　students　so　as　to　intuitively　express　them.　We　evaluated　our　method　using　the　real　behavior　data　of　undergraduates　in　a　university　in　Beijing.　The　experimental　results　indicate　that　this　method　is　effective　and　intuitive　for　student　association　analysis.　©　2020　by　the　authors.