Pollen　classification　plays　an　essential　role　in　many　fields　such　as　medicine　and　palynology.　Notably,　manual　pollen　identification　via　observing　key　pollen　information　(e.g.,　their　contours)　is　time-consuming　and　laborious.　To　date,　deep　learning　methods　can　extract　complex　features　in　an　end-to-end　manner.　However,　deep　learning　based　automatic　classification　methods　on　pollen　grains　are　still　rare,　and　their　performances　remain　unsatisfactory　owing　to　limitation　of　interference　from　irrelevant　information　(such　as　impurities　and　bubbles)　and　the　lack　of　pollen　attention.　Based　on　the　above　considerations,　we　propose　a　contour-guided　network　called　CG-Net,　which　contains　three　modules.　Image　pre-processing　module　first　removes　impurities　and　bubbles　in　pollen　images　according　to　the　color　information.　Then,　contour　awareness　module　is　designed　to　generate　contour　features　and　these　features　are　served　as　attention　maps　for　next　module.　Finally,　contour　guidance　module　weights　the　yielded　contour　attention　maps　to　both　original　images　and　feature　maps　of　different　convolution　layers,　making　the　CNN　focus　on　discriminative　features　of　pollen　grains.　Extensive　experiments　are　conducted　on　several　real-world　pollen　datasets,　and　the　results　demonstrate　the　effectiveness　of　our　proposed　method　with　the　accuracy　and　F1-score　over　84%.　©　2022　IEEE.