Airborne　allergenic　pollen　can　trigger　various　hay　fevers　such　as　seasonal　allergic　rhinitis　and　bronchial　asthma.　Accurate　and　timely　pollen　forecasting　services　play　a　crucial　role　in　enabling　individuals　with　hay　fever　to　take　preventive　measures　proactively.　Currently,　automatic　pollen　recognition　research　have　provided　new　insights　into　timely　pollen　forecasting　services.　However,　the　forecasting　results　of　existing　automatic　pollen　recognition　methods　fail　to　convince　owing　to　the　pollen　data　characteristics　in　real　scenes.　Hence,　we　fully　simulate　the　observation　strategy　of　palynologists　(namely,　localization-before-classification)　to　address　the　challenges　encountered　in　real-scenes.　This　strategy　comprises　two　key　steps:　(1)　to　determine　the　location　information　of　each　pollen　grain;　(2)　to　distinguish　the　category　information　of　pollen　grain　(using　the　key　features　of　pollen　grain,　such　as　contour,　color　and　texture).　Motivated　by　this　strategy,　we　propose　a　computer-aided　system　for　eight　airborne　allergenic　pollens　recognition　to　a　specific　area　in　Beijing　called　PBJ-Sys.　Pollen　whole-slide　imaging　images　are　utilized　as　input,　and　four　components　(Image　Prepocessing,　Multi-scale　Fusion　Pollen　Localization,　Knowledge-guided　Pollen　Classification　and　Result　Statistics)　within　the　PBJ-Sys　are　integrated　to　output　the　total　pollen　concentration　and　single　pollen　category　quantities　results.　The　PBJ-Sys　helps　to　reduce　the　burden　of　manual　microscopy,　enhance　symptom　control　and　maintain　quality　of　life　in　pollen　allergy.