In　continuous-variable　quantum　key　distribution　(CV-QKD),　weak　signal　carrying　information　transmits　from　Alice　to　Bob;　during　this　process　it　is　easily　influenced　by　unknown　noise　which　reduces　signal-to-noise　ratio,　and　strongly　impacts　reliability　and　stability　of　the　communication.　Recurrent　quantum　neural　network　(RQNN)　is　an　artificial　neural　network　model　which　can　perform　stochastic　filtering　without　any　prior　knowledge　of　the　signal　and　noise.　In　this　paper,　a　modified　RQNN　algorithm　with　expectation　maximization　algorithm　is　proposed　to　process　the　signal　in　CV-QKD,　which　follows　the　basic　rule　of　quantum　mechanics.　After　RQNN,　noise　power　decreases　about　15　dBm,　coherent　signal　recognition　rate　of　RQNN　is　96%,　quantum　bit　error　rate　(QBER)　drops　to　4%,　which　is　6.9%　lower　than　original　QBER,　and　channel　capacity　is　notably　enlarged.　©　2018,　Springer　Science+Business　Media,　LLC,　part　of　Springer　Nature.