Diabetic　retinopathy　(DR)　is　a　common　ocular　complication　in　diabetic　patients　and　is　a　major　cause　of　blindness　in　the　population.　DR　often　leads　to　progressive　changes　in　the　structure　of　the　vascular　system　and　causes　abnormalities.　In　the　process　of　DR　analysis,　the　image　quality　needs　to　be　evaluated　first,　and　images　with　better　imaging　quality　are　selected,　followed　by　value-added　proliferative　diabetic　retinopathy　(PDR)　detection.　Therefore,　in　this　paper,　the　MixNet　classification　network　was　first　used　for　image　quality　assessment　(IQA),　and　then　the　ResNet50-CMBA　network　was　used　for　DR　grading　of　images,　and　both　networks　were　combined　with　a　k-fold　cross-validation　strategy.　We　evaluated　our　method　at　the　2022　Diabetic　Retinopathy　Analysis　Challenge　(DRAC),　where　image　quality　was　evaluated　on　1103　ultra-wide　optical　coherence　tomography　angiography　(UW-OCTA)　images　and　DR　grading　was　detected　on　997　UW-OCTA　images.　Our　method　achieved　a　Quadratic　Weight　Kappa　of　0.7547　and　0.8010　in　the　test　cases,　respectively.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG.