In　this　paper,　a　new　method　for　the　classification　of　Alzheimer＇s　disease　(AD)　using　multi-feature　combination　of　structural　magnetic　resonance　imaging　is　proposed.　Firstly,　hippocampal　segmentation　and　cortical　thickness　and　volume　measurement　were　performed　using　FreeSurfer　software.　Then,　histogram,　gradient,　length　of　gray　level　co-occurrence　matrix　and　run-length　matrix　were　used　to　extract　the　three-dimensional　(3D)　texture　features　of　the　hippocampus,　and　the　parameters　with　significant　differences　between　AD,　MCI　and　NC　groups　were　selected　for　correlation　study　with　MMSE　score.　Finally,　AD,　MCI　and　NC　are　classified　and　identified　by　the　extreme　learning　machine.　The　results　show　that　texture　features　can　provide　better　classification　results　than　volume　features　on　both　left　and　right　sides.　The　feature　parameters　with　complementary　texture,　volume　and　cortical　thickness　had　higher　classification　recognition　rate,　and　the　classification　accuracy　of　the　right　side　(100%)　was　higher　than　that　of　the　left　side　(91.667%).　The　results　showed　that　3D　texture　analysis　could　reflect　the　pathological　changes　of　hippocampal　structures　of　AD　and　MCI　patients,　and　combined　with　multi-feature　analysis,　it　could　better　reflect　the　essential　differences　between　AD　and　MCI　cognitive　impairment,　which　was　more　conducive　to　clinical　differential　diagnosis.　Copyright　©　2019　by　Editorial　Office　of　Journal　of　Biomedical　Engineering.