In　the　process　of　hotel　reservation　on　online　traveling　platforms,　online　reviews,　as　a　fundamental　source　where　the　actual　information　of　a　product　can　be　had　access　to,　have　been　attached　with　high　importance　by　customers　when　they　have　difficulty　making　a　decision　on　which　hotel　to　pick.　However,　with　enormous　amount　of　online　reviews　distributed　in　diverse　online　traveling　platforms,　customers　tend　to　have　few　patience　or　time　to　manually　read　all　these　reviews　and　get　the　exact　information　they　want.　Inspired　by　the　widespread　application　of　aspect-based　sentiment　analysis　in　the　field　of　data　mining,　a　bidirectional　long　short-term　memory　(Bi-LSTM)　and　attention　mechanism　based　model　to　predict　multiple　attributes　of　a　product　from　online　review　texts　is　proposed.　Experimental　result　shows　that　such　Bi-LSTM　with　attention　mechanism　model　apparently　improves　the　accuracy　of　the　prediction,　compared　with　single　LSTM　model.　Meanwhile,　based　on　the　output　of　the　prediction,　we　analyze　and　transfer　it　into　a　statistical　matrix.　With　an　intuitionistic　fuzzy　compromise　decision-making　method　VIKOR　applied,　an　overall　ranking,　according　to　multiple　product　attributes　can　be　made,　in　which　way　to　help　customers　make　decisions.　To　prove　the　rationality　of　the　algorithm,　online　hotel　reviews　from　three　stream　online　travelling　platforms　are　crawled　as　a　case.