With　the　intensification　of　urbanization　in　human　society,　pollen　allergy　has　become　a　seasonal　epidemic　disease　with　a　considerable　incidence　rate,　seriously　affecting　the　healthy　life　of　residents.　Accurately　classifying　and　recognizing　major　allergenic　pollens　for　effective　pollen　monitoring　and　forecasting　is　of　great　practical　significance　for　improving　urban　livability　and　citizens＇　quality　of　life.　With　the　development　of　deep　learning,　automatic　classification　gradually　replaces　the　process　of　manually　recognizing　pollen　grains.　Recently,　Swin　Transformer　(SwinT)　has　demonstrated　strong　competitiveness　in　various　tasks.　In　order　to　solve　the　problem　of　low　resolution　and　complex　background　information　of　pollen　images,　we　propose　a　novel　classification　framework　titled　Swin　Transformer　with　Image　Super　-resolution　Reconstruction　Network　(SwinTSRNet)　for　pollen　images　classification.　In　the　proposed　SwinT-SRNet　network,　an　image　super　-resolution　reconstruction　method　based　on　the　Efficient　Super　-resolution　Transformer　(ESRT)　is　designed　to　eliminate　the　blurring　problem　that　arises　when　resizing　low　-resolution　images　to　fit　the　training　dimensions　of　the　SwinT　model.　Furthermore,　a　high　-frequency　(HF)　information　extraction　module　is　proposed　to　capture　highfrequency　information　in　images　to　provide　richer　information　for　the　SwinT-SRNet　classification　network.　Extensive　experimental　evaluations　on　a　self　-constructed　allergic　pollen　dataset　(POLLEN8BJ)　in　Beijing,　China,　as　well　as　a　public　pollen　dataset　POLLEN20L-det,　show　that　the　SwinT-SRNet　model　achieves　remarkable　accuracies　of　99.46%　and　98.98%.　Notably,　even　without　pre　-training　weights,　the　model　achieved　98.57%　and　98.31%　accuracy　on　the　POLLEN8BJ　and　POLLEN20L-det　datasets,　which　are　1.05%　and　1.19%　higher　than　SwinT,　respectively.