Myasthenia　gravis　(MG)　is　a　neurological　disease　that　is　difficult　to　diagnose　and　requires　long-term　management.　The　progression　of　this　disease　is　reflected　to　some　extent　in　changes　in　speech,　such　as　hoarseness　and　articulation　disorders.　However,　it　is　difficult　for　general　neurologists　to　grasp　the　diagnostic　patterns　of　such　rare　diseases,　especially　in　underdeveloped　regions.　As　an　emerging　field,　speech-based　intelligent　diagnostic　assistance　provides　a　safe,　non-invasive,　and　convenient　solution　for　healthcare　education　management.　To　this　end,　we　firstly　constructed　a　novel　Chinese　speech　dataset　of　myasthenia　gravis　patients　(MGCS).　Then　we　proposed　a　network　named　Myasthenia　Gravis　Speech　Net　(MGS-Net)　for　the　classification　of　myasthenia　gravis　pathological　speech,　which　is　mainly　composed　of　two　blocks:　the　Local　Feature　Enhancement　(LFE)　block　and　the　Feedforward　Dense　(FFD)　block.　The　LFE　block　extracts　temporal　local　features　using　a　sliding　window　approach,　while　the　FFD　block　captures　the　global　representation　of　the　data.　Compared　to　existing　methods,　our　pipeline　achieves　an　accuracy　of　98.75%　and　a　recall　rate　of　99.17%.　We　validated　the　effectiveness　of　existing　acoustic　feature　sets　in　pathological　speech　classification　of　MG,　which　will　provide　an　important　tool　for　health　education　management　of　neurological　diseases.　©　2024　IEEE.