Improving　the　accuracy　of　travel　route　recommendation　is　of　great　significance　to　the　field　of　tourism　recommendation.　Considering　the　problems　of　data　sparsity　and　cold　start　involved　in　the　process　of　travel　route　recommendation,　a　multi-objective　travel　route　recommendation　method　is　proposed　in　this　article　by　integrating　user　features　and　group　intelligence.　First,　we　obtain　scenic　spot　information　and　group　intelligence　data　from　Ctrip.com,　whlyw.net,　Baidu　Index,　and　other　websites,　including　the　locations　of　scenic　spots,　the　prices　of　scenic　spots,　user　comments,　user　ratings,　browsing　data,　etc.　Second,　we　fuse　user　features　and　the　group　intelligence　data　to　construct　the　comprehensive　attraction　index　of　scenic　spots　for　different　kinds　of　people　and　calculate　the　attraction　index　of　travel　routes.　Finally,　a　multi-objective　optimization　function　of　tourism　route　recommendation　is　defined　according　to　the　user　demand　for　the　most　efficient　access　to　scenic　spots　that　are　most　likely　to　be　of　interest　to　users　during　travel;　thus,　the　recommendation　list　is　generated　by　a　multi-objective　genetic　algorithm　(non-dominated　sorting　genetic　algorithm-II,　NSGA2).　Compared　to　traditional　travel　route　recommendation　methods,　our　proposed　method　has　following　advantages:　(1)　We　carefully　consider　the　actual　needs　of　users　(consumer　side)　and　the　attraction　of　scenic　spots　(supplier　side),　ensuring　that　users　can　visit　as　many　popular　scenic　spots　as　possible　with　less　travelling　time;　(2)　We　consider　that　scenic　spots,　especially　outdoor　scenic　spots,　show　different　attraction　to　tourists　in　different　time　periods,　and　this　paper　takes　into　account　the　travel　time　of　users　when　calculating　the　attraction　of　scenic　spots　to　users;　(3)　The　vehicle　selected　by　a　user　has　a　great　impact　on　user　travel　routes,　and　our　proposed　method　recommends　different　travel　routes　for　users　according　to　different　vehicles,　which　better　meets　user　needs;　(4)　Since　different　users　have　different　preferences　for　the　same　scenic　spot,　we　divide　user　groups　according　to　user　gender,　age,　trip　modes,　and　trip　time,　which　improves　the　accuracy　of　recommendation.　The　experimental　results　show　that:　(1)　The　factors　considered　in　this　paper　can　effectively　improve　user　satisfaction　with　the　recommendation　results;　(2)　The　proposed　method　considers　the　factors　that　can　effectively　improve　user　satisfaction　during　route　recommendation　and　provides　diverse　recommendation　results　for　users　with　different　features　and　needs;　(3)　The　proposed　method　not　only　gives　a　higher　comprehensive　attraction　index　but　also　effectively　reduces　the　time　spent　on　the　journey.　Furthermore,　the　recommendation　results　are　more　diverse,　thus　contributing　to　the　development　of　intelligent　travel　route　recommendation.　©　2022,　Science　Press.　All　right　reserved.