The　quantitative　diagnosis　of　rolling　bearing　defects　still　mainly　relies　on　the　manual　analysis　of　vibration　signals　and　is　limited　to　a　specific　moment　in　time,　which　restricts　the　intelligent　identification　of　life-cycle　defect　evolution.　In　this　article,　a　novel　digital　twin-driven　graph　convolutional　memory　network　(GCMN)　is　proposed　for　evaluating　the　defect　evolution　of　rolling　bearings　throughout　the　whole　life.　In　the　proposed　method,　a　dynamic　twin　model　is　constructed　to　generate　the　vibration　responses　that　characterize　the　state　of　bearings.　The　twin　model　is　capable　of　accurately　simulating　the　operational　conditions　of　the　bearing　and　interacting　with　the　actual　responses,　thereby　enhancing　the　accuracy　of　the　model.　In　addition,　a　graph　network　model　GCMN　is　developed　to　transfer　knowledge　from　the　twin　model　to　the　physical　entity　through　domain　adaptation,　thereby　revealing　the　relationship　between　vibration　responses　and　defect　sizes.　It　extracts　spatial　features　through　nonlinear　transformation　of　graph　data　and　incorporates　temporal　features　via　the　hidden　layer　state　at　the　previous　moment.　The　experimental　results　demonstrate　that　the　proposed　method　accurately　characterizes　the　local　defect　extension　throughout　the　bearing　entire　lifespan.