Diabetic　retinopathy　(DR)　is　a　common　ocular　disease　in　diabetic　patients.　In　DR　analysis,　doctors　first　need　to　select　excellent-quality　images　of　ultra　wide　optical　coherence　tomography　imaging　(UW-OCTA).　Only　high-quality　images　can　be　used　for　lesion　segmentation　and　proliferative　diabetic　retinopathy　(PDR)　detection.　In　practical　applications,　UW-OCTA　has　a　small　number　of　images　with　poor　quality,　so　the　dataset　constructed　from　UW-OCTA　faces　the　problem　of　class-imbalance.　In　this　work,　we　employ　data　enhancement　strategy　and　develop　a　loss　function　to　alleviate　class-imbalance.　Specifically,　we　apply　Fourier　Transformation　to　the　poor　quality　data　with　limited　numbers,　thus　expanding　this　category　data.　We　also　utilize　characteristics　of　class-imbalance　to　improve　the　cross-entropy　loss　by　weighting.　This　method　is　evaluated　on　DRAC2022　dataset,　we　achieved　Quaratic　Weight　Kappa　of　0.7647　and　AUC　of　0.8458,　respectively.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG.