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　over　time.The　system　was　evaluated　based　on　the　collected　patient　data.The　results　show　that　the　segmentation　model　achieves　a　mean　Intersection-Over-Union(mloU)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.