IEEEEffective　location　recommendation　is　an　important　problem　in　both　research　and　industry.　Much　research　has　focused　on　personalized　recommendation　for　users.　However,　there　are　more　uses　such　as　site　selection　for　firms　and　factories.　In　this　study,　we　try　to　solve　site　selection　problem　by　recommending　some　locations　satisfying　special　requirements.　There　are　many　factors　affecting　it,　including　functions　of　architecture,　building　cost,　pollution　discharge　etc.　We　focus　on　the　specific　site　selection　of　meteorological　observation　stations　in　this　paper　with　leveraging　the　factors　of　functions　of　architecture　and　building　cost　from　multi-source　urban　big　data.　We　consider　not　only　recommending　the　locations　that　can　provide　more　accuracy　prediction　and　cover　more　areas,　but　also　minimizing　the　cost　of　building　new　stations.　We　design　an　extensible　two-stage　framework　for　the　station　placing　including　prediction　model　and　recommendation　model.　It　is　very　convenient　for　executives　to　add　more　real-life　factors　into　our　approach.　We　have　some　empirical　findings　and　evaluate　the　proposed　approach　using　the　real　meteorological　data　of　Shaanxi　province,　China.　Experiment　results　show　the　better　performance　of　our　approach　than　existing　commonly　used　methods.