Effective　prediction　of　machinery　remaining　useful　life　(RUL)　is　prominent　to　achieve　intelligent　preventive　maintenance　in　manufacturing　systems.　In　this　paper,　a　sparse　graph　structure　fusion　convolutional　network　(SGSFCN)　is　proposed　for　more　accurate　end-to-end　RUL　prediction　of　machine.　A　novel　node-level　graph　structure　called　time　series　shapelet　distance　graph　(TSSDG)　is　designed　to　convert　the　time　series　to　node　feature.　The　SGSFCN　model　is　proposed　to　learn　degradation　information　from　the　graph　structure.　In　SGSFCN,　a　sparse　graph　structure　(SGS)　layer　and　a　fusion　graph　structure　(FGS)　layer　preceding　the　graph　convolutional　network　(GCN)　are　designed　to　learn　the　SGS　from　node　representation　and　fuse　the　original　graph　structure,　enabling　the　graph　structure　and　node　update　iteratively　in　subsequent　layers.　Concurrently,　a　bidirectional　long　short-term　memory　network　(BiLSTM)　layer　is　integrated　to　capture　the　global　temporal　dependencies.　The　method　is　validated　by　two　test　rig　data,　and　results　demonstrate　that　the　proposed　method　offers　significantly　higher　prediction　accuracy　of　RUL　compared　to　several　state-of-art　methods.