Interest　is　an　important　concept　in　psychology　and　pedagogy　and　is　widely　studied　in　many　fields.　Especially　in　recent　years,　the　widespread　use　of　many　interest-based　recommendation　systems　has　greatly　promoted　research　on　interest　modeling　and　mining　on　social　networks.　However,　the　existing　studies　have　rarely　tried　to　explore　the　relationships　among　interests　and　their　application　value,　and　most　similar　studies　analyze　user　behavior　data.　In　this　paper,　we　propose　and　verify　two　hypotheses　about　the　interests　of　social　network　users.　We　then　use　association　rules　to　mine　users＇　interests　from　LinkedIn　users＇　profiles.　Finally,　based　on　the　interest　association　rules　and　user　interest　distribution　on　Twitter,　we　design　an　approach　to　mine　interests　for　Twitter　users　and　conduct　two　experiments　to　systematically　demonstrate　the　approach＇s　effectiveness.　According　to　our　research,　we　found　that　there　are　a　large　number　of　association　rules　between　human　interests.　These　rules　play　a　considerable　role　in　our　method　of　interest　mining.　Our　research　work　not　only　provides　new　ideas　for　interest　mining　but　also　reveals　the　internal　relationship　between　interest　and　its　application　value.　The　research　work　has　certain　theoretical　and　practical　value.