Purpose:　This　article　aims　to　provide　an　integrated　model　and　methodology　for　discovering　valuable　innovation　knowledge　and　creative　users　in　online　innovative　communities.　Design/methodology/approach:　The　super-network　integration　approach　is　applied　in　constructing　the　user＇s　innovation　knowledge　super-network　(UIKSN)　model　based　on　knowledge　fragments　discovered　from　user-generated　content　(UGC)　data　using　text　mining　methods.　The　social　network　analysis　(SNA)　methodology　is　then　used　to　analyze　the　KSN　model.　An　empirical　research　is　conducted　with　China＇s　Xiaomi　online　community　to　illustrate　the　UIKSN　model　and　the　SNA　methodology.　Findings:　Compared　with　current　methods,　the　proposed　KSN　model　and　SNA　methodology　are　demonstrated　to　be　more　effective　and　valid　in　discovering　valuable　innovation　knowledge　including　valuable　innovations,　innovation　trends,　creative　users　and　their　knowledge　background,　etc.　Research　limitations/implications:　The　results　of　KSN　modeling　and　analysis　are　inevitably　influenced　by　the　performance　of　text　mining　methods.　Practical　implications:　More　and　more　companies　start　to　adopt　online　community　user＇s　innovations　in　their　new　product　developments.　However,　it　is　difficult　to　discover　users＇　opinions　and　innovations　from　the　UGC　due　to　its　tremendous　volume.　Therefore,　the　KSN　model　and　SNA　methodology　presented　in　this　article　are　helpful　for　companies　to　manage　online　communities　and　to　utilize　users＇　innovations　effectively　and　efficiently.　Originality/value:　The　article　provides　two　main　contributions　in　the　study　of　online　communities:　1)　a　well-justified　model　(users＇　innovation　KSN,　UIKSN)　to　explore　online　users＇　contributions;　2)　a　new　and　more　effective　methodology　to　discover　and　analyze　valuable　innovations　and　innovative　users.　©　1988-2012　IEEE.