Mild　cognitive　impairment　(MCI)　is　a　transition　stage　between　normal　aging　and　dementia.　Brain　network　has　been　proven　to　occupy　an　important　role　in　the　study　of　differences　in　Alzheimer＇s　disease　(AD)　and　MCI.　However,　there　is　little　knowledge　about　individual　metabolic　network　abnormities　which　might　be　sensitive　features　in　the　prediction　of　MCI　progression.　In　this　paper,　we　constructed　the　individual　metabolic　network　based　on　longitudinal　Fluorodeoxyglucose　positron　emission　tomography　(FDG-PET)　of　33　progress　MCI　(pMCI)　patients　and　46　stable　MCI　(sMCI)　patients　from　the　Alzheimer＇s　disease　Neuroimaging　Initiative　(ADNI).　Firstly,　PET　images　of　each　time　point　are　normalized　with　the　Yakushev　normalization　procedure　and　registered　to　the　Brainnetome　Atlas　(BNA)　template.　Then　the　combination　of　rough　distance　and　precision　distance　is　utilized　for　accurate　evaluation　of　between-region　dissimilarity　and　calculated　the　correlation　coefficient　of　the　network.　Finally,　correlative　feature　selected　by　Lasso　shows　a　significant　promotion　in　classification　performance　compare　with　the　metabolic　intensity,　achieving　an　accuracy　of　89.9%　and　area　under　the　receiver　operating　characteristic　curve　of　0.892.　What＇s　more,　the　combination　of　multi　time　points　also　suggests　a　better　classification　result　than　single　time　point.　This　finding　may　predict　disease　course　in　individuals　with　mild　cognitive　impairment.