Pollen　allergy　is　a　common　seasonal　disease.　Current　automatic　pollen　classification　methods　have　high　accuracy　for　high-quality　images.　However,　the　imaging　process　of　pollen　can　produce　defocusing　blur,　and　some　pollen　characteristics　have　subtle　differences,　which　limits　the　accuracy　of　the　methods.　To　address　this,　we　introduce　a　supervised　contrastive　learning　method　to　fine-tune　the　pre-trained　classification　network　and　optimize　its　feature　representation　ability.　We　combined　multilayer　scan　images　of　the　same　pollen　into　positive　example　pairs,　and　with　multilayer　scan　images　of　other　classes　of　pollen　into　negative　example　pairs.　This　can　learn　more　similar　features　of　the　same　class　samples　and　differences　of　different　class　samples.　We　establish　a　pollen　dictionary　based　on　feature　representation,　compare　new　test　samples　with　pollen　grains　in　the　dictionary,　and　obtain　the　final　pollen　class　through　weighted　voting.　Experimental　results　show　that　our　method　is　effective　for　distinguishing　easily　confused　pollen　grains.　©　2024　ACM.