Dictionary　learning　has　gradually　attracted　attention　due　to　its　powerful　feature　representation　ability.　However,　the　time　-shift　property　of　collected　signals　hinders　the　recognition　of　various　bearing　states.　In　addition,　existing　dictionary　learning　methods　are　mostly　designed　based　on　a　single　domain,　while　common　data　fusion　methods　used　in　data　-driven　cannot　be　directly　extended　to　dictionary　learning.　In　this　paper,　a　novel　adaptive　generalized　domain　data　fusion　-driven　kernel　sparse　representation　classification　method　(AGDFDK-SRC)　is　proposed.　First,　to　avoid　the　effect　of　the　time　-shift　property　on　dictionary　learning,　a　class　-specific　kernel　sub　-dictionary　learning　method　is　proposed,　by　which　the　non-linear　signal　data　is　mapped　into　high　-dimensional　feature　space　via　a　kernel　trick.　Second,　the　class　-specific　kernel　sub　-dictionaries　are　learned　by　kernel　K　-singular　value　decomposition　in　a　data　-driven　manner.　Then,　an　adaptive　generalized　domain　data　fusion　strategy　is　developed　for　dictionary　learning,　which　implements　data　fusion　of　multiple　domain　signals　to　enhance　the　feature　mining　ability　and　representation　ability　of　the　learned　dictionary.　Finally,　a　kernel　sparse　classification　method　is　designed　to　achieve　intelligent　bearing　fault　diagnosis.　Two　bearing　datasets　are　exploited　to　verify　the　recognition　performance　of　AGDFDK-SRC,　indicating　that　the　AGDFDK-SRC　obtains　superior　average　classification　accuracies　of　98.23%　and　99.50%,　respectively.