Accurate　segmentation　of　hard　exudates　in　early　non-proliferative　diabetic　retinopathy　can　assist　physicians　in　taking　appropriate　treatment　in　a　more　targeted　manner,　in　order　to　avoid　more　serious　damage　to　vision　caused　by　the　deterioration　of　the　disease　in　the　later　stages.　Here,　an　Adaptive　Learning　Unet-based　adversarial　network　with　Convolutional　neural　network　and　Transformer　(CT-ALUnet)　is　proposed　for　automatic　segmentation　of　hard　exudates,　combining　the　excellent　local　modelling　ability　of　Unet　with　the　global　attention　mechanism　of　transformer.　Firstly,　multi-scale　features　are　extracted　through　a　CNN　dual-branch　encoder.　Then,　the　information　fusion　of　features　at　adjacent　scale　is　realized　and　the　fused　features　are　selected　adaptively　to　maintain　the　overall　consistency　of　features　by　attention-guided　multi-scale　fusion　blocks　(AGMFB).　After　that,　the　high-level　encoded　features　are　input　to　transformer　blocks　to　extract　global　contexts.　Finally,　these　features　are　fused　layer-by-layer　to　achieve　accurate　segmentation　of　hard　exudates.　In　addition,　adversarial　training　is　incorporated　into　the　above　segmentation　model,　which　improves　Dice　scores　and　MIoU　scores　by　7.5%　and　3%,　respectively.　Experiments　demonstrate　that　CT-ALUnet　shows　more　reliable　segmentation　and　stronger　generalization　ability　than　other　SOTA　methods,　which　lays　a　good　foundation　for　computer-assisted　diagnosis　and　assessment　of　efficacy.