The　student　group　division　is　benefit　for　universities　to　do　the　student　management　based　on　the　group　profile.　With　the　widespread　use　of　student　smart　cards　on　campus,　especially　where　students　living　in　campus　residence　halls,　students＇　daily　activities　on　campus　are　recorded　with　information　such　as　smart　card　swiping　time　and　location.　Therefore,　it　is　feasible　to　depict　the　students　with　the　daily　activity　data　and　accordingly　group　students　based　on　objective　measuring　from　their　campus　behavior　with　some　regular　student　attributions　collected　in　the　management　system.　However,　it　is　challenge　in　feature　representation　due　to　diverse　forms　of　the　student　data.　To　effectively　and　comprehensively　represent　students＇　behaviors　for　further　student　group　division,　we　proposed　to　adopt　activity　data　from　student　smart　cards　and　student　attributes　as　input　data　with　taking　account　of　activity　and　attribution　relationship　types　from　different　perspective.　Specially,　we　propose　a　novel　student　group　division　method　based　on　a　multi-view　student　attribute　heterogeneous　information　network　(MSA-HIN).　The　network　nodes　in　our　proposed　MSA-HIN　represent　students　with　their　multi-dimensional　attribute　information.　Meanwhile,　the　edges　are　constructed　to　characterize　student　different　relationships,　such　as　co-major,　co-occurrence,　and　co-borrowing　books.　Based　on　the　MSA-HIN,　embedded　representations　of　students　are　learned　and　a　deep　graph　cluster　algorithm　is　applied　to　divide　students　into　groups.　Comparative　experiments　have　been　done　on　a　real-life　campus　dataset　collected　from　a　university.　The　experimental　results　demonstrate　that　our　method　can　effectively　reveal　the　variability　of　student　attributes　and　relationships　and　accordingly　achieves　the　best　clustering　results　for　group　division.　©　2022　Korean　Society　for　Internet　Information.　All　rights　reserved.