Long　non-coding　RNAs　(LncRNAs)　play　important　roles　in　a　series　of　life　activities,　and　they　function　primarily　with　proteins.　The　wet　experimental-based　methods　in　lncRNA-protein　interactions　(lncRPIs)　study　are　timeconsuming　and　expensive.　In　this　study,　we　propose　for　the　first　time　a　novel　feature　fusion　method,　the　LPICSFFR,　to　train　and　predict　LncRPIs　based　on　a　Convolutional　Neural　Network　(CNN)　with　feature　reuse　and　serial　fusion　in　sequences,　secondary　structures,　and　physicochemical　properties　of　proteins　and　lncRNAs.　The　experimental　results　indicate　that　LPI-CSFFR　achieves　excellent　performance　on　the　datasets　RPI1460　and　RPI1807　with　an　accuracy　of　83.7　%　and　98.1　%,　respectively.　We　further　compare　LPI-CSFFR　with　the　state-ofthe-art　existing　methods　on　the　same　benchmark　datasets　to　evaluate　the　performance.　In　addition,　to　test　the　generalization　performance　of　the　model,　we　independently　test　sample　pairs　of　five　model　organisms,　where　Mus　musculus　are　the　highest　prediction　accuracy　of　99.5　%,　and　we　find　multiple　hotspot　proteins　after　constructing　an　interaction　network.　Finally,　we　test　the　predictive　power　of　the　LPI-CSFFR　for　sample　pairs　with　unknown　interactions.　The　results　indicate　that　LPI-CSFFR　is　promising　for　predicting　potential　LncRPIs.　The　relevant　source　code　and　the　data　used　in　this　study　are　available　at　https://github.com/JianjunTan-Beijing/LPICSFFR.