Quality　assessment　of　ultra-wide　optical　coherence　tomography　angiography　(UW-OCTA)　images　followed　by　lesion　segmentation　and　proliferatived　diabetic　retinopathy　(PDR)　detection　is　of　great　significance　for　the　diagnosis　of　diabetic　retinopathy.　However,　due　to　the　complexity　of　UW-OCTA　images,　it　is　challenging　to　achieve　automatic　image　quality　assessment　and　PDR　detection　in　a　limited　dataset.　This　work　presented　a　fully　automated　convolutional　neural　network-based　method　for　image　quality　assessment　and　retinopathy　grading.　In　the　first　stage,　the　dataset　was　augmented　to　eliminate　the　category　imbalance　problem.　In　the　second　stage,　the　EfficientNet-B2　network,　pre-trained　on　ImageNet,　was　used　for　quality　assessment　and　lesion　grading　of　UW-OCTA　images.　We　evaluated　our　method　on　the　DRAC2022　dataset.　A　quadratic　weighted　kappa　score　of　0.7704　was　obtained　on　the　task　2　image　quality　assessment　test　set　and　0.8029　on　the　task　3　retinopathy　grading　test　set.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG.