The　remaining　useful　life　(RUL)　prediction　of　rolling　element　bearings　is　usually　subject　to　the　following　limitations.　First,　it　is　difficult　to　obtain　the　massive　performance　degradation　data,　which　resulting　in　the　insufficient　learning　of　the　historical　degradation　law.　Second,　the　parameters　in　most　of　existing　models　depend　heavily　on　the　manual　selection,　which　leads　to　the　poor　generalization　performance.　To　address　these　problems,　a　novel　adaptive　sparse　graph　learning　(ASGL)　method　based　on　digital　twin　dictionary　(DTD)　is　proposed　in　this　article.　To　facilitate　the　prediction　when　the　data　are　insufficient,　the　extended　exponential　models　and　the　extended　linear　piecewise　models　are　first　established,　then　a　DTD　that　covers　the　various　degradation　behaviors　is　constructed.　Besides,　a　new　objective　function　of　graph　learning　is　designed　and　the　sparse　regularization　method　is　introduced　to　adaptively　obtain　the　topology　graph　of　data.　Therefore,　the　method　avoids　the　wrong　adjacency　relationship　caused　by　inappropriate　parameters.　The　simulation　and　experimental　results　show　that　the　DTD　has　higher　prediction　accuracy　than　the　experimental　samples,　and　the　ASGL　method　is　easy　to　implement　and　has　lower　dependence　on　the　parameter　selections.　In　addition,　compared　with　some　state-of-the-art　methods,　it　can　obtain　better　RUL　prediction　results.