The　increasing　E-tourism　systems　provide　intelligent　tour　recommendation　for　tourists.　In　this　sense,　recommender　system　can　make　personalized　suggestions　and　provide　satisfied　information　associated　with　their　tour　cycle.　Data　mining　is　a　proper　tool　that　extracting　potential　information　from　large　database　for　making　strategic　decisions.　In　the　study,　association　rule　analysis　based　on　FP-growth　algorithm　is　applied　to　find　the　association　relationship　among　scenic　spots　in　different　cities　as　tour　route　recommendation.　In　order　to　figure　out　valuable　rules,　Kulczynski　interestingness　measure　is　adopted　and　imbalance　ratio　is　computed.　The　proposed　scheme　was　evaluated　on　Wangluzhe　cultural　tourism　service　network　operation　platform　(WCTSNOP),　where　it　could　verify　that　it　is　able　to　quick　recommend　tour　route　and　to　rapidly　enhance　the　recommendation　quality.　©　Authors　2017.　CC　BY　4.0　License.