Amnestic　mild　cognitive　impairment　(MCI)　commonly　represents　an　intermediate　stage　situated　in　the　spectrum　between　normal　age-related　cognitive　decline　and　dementia.　Predicting　of　MCI　conversion　to　Alzheimer＇s　Disease　(AD)　plays　critical　roles　in　early　diagnosis　and　disease-modifying　therapies.　We　analyzed　baseline　3T　MRI　scans　in　337　MCI　patients　from　the　ADNI-GO　and　ANDI-2　cohorts.　The　subjects　were　divided　into　MCI　non-converters　(MCInc)　and　MCI　converters　(MCIc).　To　evaluate　conversion　rates,　we　aim　to　first　extract　intermediate　representations　of　structural　MRI　(sMRI)　by　a　pre-trained　convolutional　neural　network　(CNN)　model,　then　combine　principal　component　analysis　(PCA)　and　sequential　feature　selection　(SFS)　for　feature　selection,　and　finally　adopt　support　vector　machine　(SVM)　for　prediction.　The　method　attained　an　accuracy　of　77.58%,　a　sensitivity　of　90.48%,　a　specificity　of　76.42%,　which　may　be　useful　and　practical　for　clinical　diagnosis.