This　paper　proposes　a　linear　prediction-based　part-defined　auto-encoder　(PAE)　network　to　enhance　speech　signal.　The　PAE　is　a　defined　decoder　or　a　defined　encoder　network,　based　on　efficient　learning　algorithm　or　classical　model.　In　this　paper,　the　PAE　utilizes　AR-Wiener　filter　as　decoder　part,　and　the　AR-Wiener　filter　is　modified　as　a　linear　prediction　(LP)　model　by　incorporating　the　modified　factor　from　residual　signal.　The　parameters　of　line　spectral　frequency　(LSF)　of　speech　and　noise　and　the　Wiener　filtering　mask　are　utilized　for　training　targets.　Finally,　the　proposed　the　LP-based　PAE　is　compared　with　the　baseline　method,　namely　the　Wiener　filtering　mask-based　DNN.　The　PESQ　and　STOI　results　of　the　LP-based　PAE　are　better　than　baseline　method　at　lower　signal　noise　ratio　(SNR)　levels.