Pollen　allergies　are　seasonal　epidemic　diseases　that　are　accompanied　by　high　incidence　rates,　especially　in　Beijing,　China.　With　the　development　of　deep　learning,　key　progress　has　been　made　in　the　task　of　automatic　pollen　grain　classification,　which　could　replace　the　time-consuming　and　laborious　manual　identification　process　using　a　microscope.　In　China,　few　pioneering　works　have　made　significant　progress　in　automatic　pollen　grain　classification.　Therefore,　we　first　constructed　a　multi-class　and　large-scale　pollen　grain　dataset　for　the　Beijing　area　in　preparation　for　the　task　of　pollen　classification.　Then,　a　deblurring　pipeline　was　designed　to　enhance　the　quality　of　the　pollen　grain　images　selectively.　Moreover,　as　pollen　grains　vary　greatly　in　size　and　shape,　we　proposed　an　easy-to-implement　and　efficient　multi-scale　deep　learning　architecture.　Our　experimental　results　showed　that　our　architecture　achieved　a　97.7%　accuracy,　based　on　the　Resnet-50　backbone　network,　which　proved　that　the　proposed　method　could　be　applied　successfully　to　the　automatic　identification　of　pollen　grains　in　Beijing.