Various　remaining　useful　life　(RUL)　prediction　methods,　encompassing　model-based,　data-driven,　and　hybrid　methods,　have　been　developed　and　successfully　applied　to　prognostics　and　health　management　for　diverse　rolling　bearing.　Hybrid　methods　that　integrate　the　merits　of　model-based　and　data-driven　methods　have　garnered　significant　attention.　However,　the　effective　integration　of　the　two　methods　to　address　the　randomness　in　rolling　bearing　full　life　cycle　processes　remains　a　significant　challenge.　To　overcome　the　challenge,　this　paper　proposes　a　data　and　model　synergy-driven　RUL　prediction　framework　that　includes　two　data　and　model　synergy　strategies.　First,　a　convolutional　stacked　bidirectional　long　short-term　memory　network　with　temporal　attention　mechanism　is　established　to　construct　Health　Index　(HI).　The　RUL　prediction　is　achieved　based　on　HI　and　polynomial　model.　Second,　a　three-phase　degradation　model　based　on　the　Wiener　process　is　developed　by　considering　the　evolutionary　pattern　of　different　degradation　phases.　Then,　two　synergy　strategies　are　designed.　Strategy　1:　HI　is　adopted　as　the　observation　value　for　online　updating　of　physics　degradation　model　parameters　under　Bayesian　framework,　and　the　RUL　prediction　results　are　obtained　from　the　physics　degradation　model.　Strategy　2:　The　RUL　prediction　results　from　the　data-driven　and　physics-based　model　are　weighted　linearly　combined　to　improve　the　overall　prediction　accuracy.　The　effectiveness　of　the　proposed　model　is　verified　using　two　bearing　full　life　cycle　datasets.　The　results　indicate　that　the　proposed　approach　can　accommodate　both　short-term　and　long-term　RUL　predictions,　outperforming　state-of-the-art　single　models.