The　current　mode　of　clinical　aided　diagnosis　of　Ocular　Myasthenia　Gravis　(OMG)　is　time-consuming　and　laborious,　and　it　lacks　quantitative　standards.　An　aided　diagnostic　system　for　OMG　is　proposed　to　solve　this　problem.　The　values　calculated　by　the　system　include　three　clinical　indicators:　eyelid　distance,　sclera　distance,　and　palpebra　superior　fatigability　test　time.　For　the　first　two　indicators,　the　semantic　segmentation　method　was　used　to　extract　the　pathological　features　of　the　patient＇s　eye　image　and　a　semantic　segmentation　model　was　constructed.　The　patient　eye　image　was　divided　into　three　regions:　iris,　sclera,　and　background.　The　indicators　were　calculated　based　on　the　position　of　the　pixels　in　the　segmentation　mask.　For　the　last　indicator,　a　calculation　method　based　on　the　Eyelid　Aspect　Ratio　(EAR)　is　proposed;　this　method　can　better　reflect　the　change　of　eyelid　distance　overtime.　The　system　was　evaluated　based　on　the　collected　patient　data.　The　results　show　that　the　segmentation　model　achieves　a　mean　Intersection-Over-Union　(mIoU)　value　of　86.05%.　The　paired-sample　T-test　was　used　to　compare　the　results　obtained　by　the　system　and　doctors,　and　the　p　values　were　all　greater　than　0.05.　Thus,　the　system　can　reduce　the　cost　of　clinical　diagnosis　and　has　high　application　value.