Diabetic　retinopathy　(DR)　is　a　chronic　complication　of　diabetes　that　damages　the　retina　and　is　one　of　the　leading　causes　of　blindness.　In　the　process　of　diabetic　retinopathy　analysis,　it　is　necessary　to　first　assess　the　quality　of　images　and　select　the　images　with　better　imaging　quality.　Then　DR　analysis,　such　as　DR　grading,　is　performed.　Therefore,　it　is　crucial　to　implement　a　flexible　and　robust　method　to　achieve　automatic　image　quality　assessment　and　DR　grading.　In　deep　learning,　due　to　the　high　complexity,　weak　individual　differences,　and　noise　interference　of　ultra-wide　optical　coherence　tomography　angiography　(UW-OCTA)　images,　individual　classification　networks　have　not　been　able　to　achieve　satisfactory　accuracy　on　such　tasks　and　do　not　generalize　well.　Therefore,　in　this　work,　we　use　multiple　models　ensemble　methods,　by　ensemble　different　baseline　networks　of　RegNet　and　EfficientNetV2,　which　can　simply　and　significantly　improve　the　prediction　accuracy　and　robustness.　A　transfer　learning　based　solution　is　proposed　for　the　problem　of　insufficient　diabetic　image　data　for　retinopathy.　After　doing　feature　enhancement　on　the　images,　the　UW-OCTA　image　task　will　be　fine-tuned　by　combining　the　network　pre-trained　with　ImageNet　data.　our　method　achieves　a　quadratic　weighted　kappa　of　0.778　and　AUC　of　0.887　in　image　quality　assessment　(IQA)　and　0.807　kappa　and　AUC　of　0.875　in　diabetic　retinopathy　grading.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG.