Microscopic　imaging　plays　an　important　role　in　the　biomedical　field.　Existing　deep　learning　based　methods　rely　on　high-quality　data.　However,　there　is　a　lot　of　noise　(such　as　bubbles　and　impurities)　in　the　microscopic　images　of　biological　samples　collected　outdoors,　which　may　lead　to　significant　interference　in　the　microscopic　objects　identification　task.　To　solve　this　problem,　this　paper　proposes　a　deep　learning　based　method　for　microscopic　object　localization　and　classification.　Firstly,　the　whole　slide　image　is　preprocessed　to　obtain　the　microscopic　images　after　preliminary　filtering　bubbles　and　impurities.　Then,　the　sensitized　pollen　grains　are　located　based　on　the　deep　learning　model　to　remove　the　interference　of　remaining　impurities,　and　the　microscopic　images　of　sensitized　pollen　grains　are　classified.　This　method　can　effectively　suppress　the　interference　of　noise　in　microscopic　images　on　object　classification　and　improve　the　accuracy　and　reliability　of　model.　The　proposed　method　is　verified　by　experiments　based　on　real　data　and　the　results　show　that　the　proposed　method　achieves　the　highest　accuracy　compared　with　other　deep　learning　methods.