Purpose:　Automatic　multilabel　classification　of　multiple　fundus　diseases　is　of　impor-tance　for　ophthalmologists.　This　study　aims　to　design　an　effective　multilabel　classification　model　that　can　automatically　classify　multiple　fundus　diseases　based　on　color　fundus　images.　Methods:　We　proposed　a　multilabel　fundus　disease　classification　model　based　on　a　convolutional　neural　network　to　classify　normal　and　seven　categories　of　common　fundus　diseases.　Specifically,　an　attention　mechanism　was　introduced　into　the　network　to　further　extract　information　features　from　color　fundus　images.　The　fundus　images　with　eight　categories　of　labels　were　applied　to　train,　validate,　and　test　our　model.　We　employed　the　validation　accuracy,　area　under　the　receiver　operating　characteristic　curve　(AUC),　and　F1-score　as　performance　metrics　to　evaluate　our　model.　Results:　Our　proposed　model　achieved　better　performance　with　a　validation　accuracy　of　94.27%,　an　AUC　of　85.80%,　and　an　F1-score　of　86.08%,　compared　to　two　state-of-the-art　models.　Most　important,　the　number　of　training　parameters　has　dramatically　dropped　by　three　and　eight　times　compared　to　the　two　state-of-the-art　models.　Conclusions:　This　model　can　automatically　classify　multiple　fundus　diseases　with　not　only　excellent　accuracy,　AUC,　and　F1-score　but　also　significantly　fewer　training　parameters　and　lower　computational　cost,　providing　a　reliable　assistant　in　clinical　screening.　Translational　Relevance:　The　proposed　model　can　be　widely　applied　in　large-scale　multiple　fundus　disease　screening,　helping　to　create　more　efficient　diagnostics　in　primary　care　settings.　©　2023　The　Authors.