Introduction　Mild　cognitive　impairment　(MCI)　is　an　important　stage　in　Alzheimer＇s　disease　(AD)　research,　focusing　on　early　pathogenic　factors　and　mechanisms.　Examining　MCI　patient　subtypes　and　identifying　their　cognitive　and　neuropathological　patterns　as　the　disease　progresses　can　enhance　our　understanding　of　the　heterogeneous　disease　progression　in　the　early　stages　of　AD.　However,　few　studies　have　thoroughly　analyzed　the　subtypes　of　MCI,　such　as　the　cortical　atrophy,　and　disease　development　characteristics　of　each　subtype.Methods　In　this　study,　396　individuals　with　MCI,　228　cognitive　normal　(CN)　participants,　and　192　AD　patients　were　selected　from　ADNI　database,　and　a　semi-supervised　mixture　expert　algorithm　(MOE)　with　multiple　classification　boundaries　was　constructed　to　define　AD　subtypes.　Moreover,　the　subtypes　of　MCI　were　obtained　by　using　the　multivariate　linear　boundary　mapping　of　support　vector　machine　(SVM).　Then,　the　gray　matter　atrophy　regions　and　severity　of　each　MCI　subtype　were　analyzed　and　the　features　of　each　subtype　in　demography,　pathology,　cognition,　and　disease　progression　were　explored　combining　the　longitudinal　data　collected　for　2　years　and　analyzed　important　factors　that　cause　conversion　of　MCI　were　analyzed.Results　Three　MCI　subtypes　were　defined　by　MOE　algorithm,　and　the　three　subtypes　exhibited　their　own　features　in　cortical　atrophy.　Nearly　one-third　of　patients　diagnosed　with　MCI　have　almost　no　significant　difference　in　cerebral　cortex　from　the　normal　aging　population,　and　their　conversion　rate　to　AD　are　the　lowest.　The　subtype　characterized　by　severe　atrophy　in　temporal　lobe　and　frontal　lobe　have　a　faster　decline　rate　in　many　cognitive　manifestations　than　the　subtype　featured　with　diffuse　atrophy　in　the　whole　cortex.　APOE　epsilon　4　is　an　important　factor　that　cause　the　conversion　of　MCI　to　AD.Conclusion　It　was　proved　through　the　data-driven　method　that　MCI　collected　by　ADNI　baseline　presented　different　subtype　features.　The　characteristics　and　disease　development　trajectories　among　subtypes　can　help　to　improve　the　prediction　of　clinical　progress　in　the　future　and　also　provide　necessary　clues　to　solve　the　classification　accuracy　of　MCI.