Social　relationships　among　students　at　campus　are　closely　related　to　their　mental　health　and　academic　performance.　Therefore,　it　is　a　very　important　task　for　educators　to　analyze　students＇　social　relationships.　However,　existing　studies　have　focused　on　one-to-one　social　relationships　between　students,　few　ones　have　explored　the　high-order　community　relationships　hidden　in　social　networks,　especially　in　a　visual　manner.　To　solve　this　problem,　a　visual　analysis　system　called　ViSSR　is　proposed　in　this　paper,　which　utilizes　the　Louvain　algorithm　to　detect　the　hierarchical　community　structure　of　students＇　social　network　at　campus,　and　then　provides　four　coordinated　views　to　visualize　the　detection　results.　Among　the　views,　the　hierarchical　hypergraph　view　is　to　visualize　the　hierarchical　community　structure　that　greatly　breaks　through　the　limitations　of　first-order　relationships　available　in　a　traditional　node-link　social　network,　the　community　analysis　view　and　individual　analysis　view　show　the　social　characteristics　of　a　community　and　individual　student　respectively,　and　the　matrix　view　displays　the　behavioral　features　of　students.　Case　studies　and　experts　evaluation　have　been　conducted　to　demonstrate　the　usability　of　the　system.