Purpose　Fundus　lesion　segmentation　determines　the　location　and　size　of　diabetes　retinopathy　in　fundus　image,　which　assists　doctors　in　developing　the　best　eye　treatment　plan.　However,　owing　to　the　scattered　distribution　and　the　similarity　of　lesions,　it　is　extremely　difficult　to　extract　representative　lesions　feature　and　accurately　segment　lesions　area.Methods　To　solve　the　thorny　problem,　a　generative　adversarial　network　with　multi-attention　feature　extraction　is　developed　to　segment　diabetic　retinopathy　region.　The　main　contributions　are　as　follows:　(1)　An　improved　residual　U-Net　network　combining　with　self-attention　mechanism　is　designed　as　generative　network　to　fully　extract　local　and　global　feature　of　lesions　while　reducing　the　loss　of　key　feature　information.　Considering　the　correlation　between　the　same　lesions　feature　of　different　samples,　external　attention　mechanism　is　introduced　in　the　residual　U-Net　network　to　focus　on　the　relevant　features　of　the　same　lesions　in　different　samples　throughout　the　entire　dataset.　(2)　A　discriminative　network　based　on　the　PatchGAN　structure　is　designed　to　further　enhance　the　segmentation　ability　of　generation　network　by　discriminating　between　true　and　false　samples.Results　The　proposed　network　is　evaluated　on　the　public　dataset　IDRiD,　which　achieved　the　Dice　correlation　coefficients　of　75.7%,　76.53%,　50.06%,　and　45.89%　for　EX,　SE,　MA,　and　HE,　respectively.Conclusion　The　experimental　results　show　the　generative　adversarial　neural　network　qualified　for　accurate　segmentation　of　diabetic　retinopathy　from　fundus　image　well.