The　fetal　electrocardiogram　(FECG)　records　changes　in　the　graph　of　fetal　cardiac　action　potential　during　conduction,　reflecting　the　developmental　status　of　the　fetus　in　utero　and　its　physiological　cardiac　activity.　Morphological　alterations　in　the　FECG　can　indicate　intrauterine　hypoxia,　fetal　distress,　and　neonatal　asphyxia　early　on,　enhancing　maternal　and　fetal　safety　through　prompt　clinical　intervention,　thereby　reducing　neonatal　morbidity　and　mortality.　To　reconstruct　FECG　signals　with　clear　morphological　information,　this　paper　proposes　a　novel　deep　learning　model,　CBLS-CycleGAN.　The　model＇s　generator　combines　spatial　features　extracted　by　the　CNN　with　temporal　features　extracted　by　the　BiLSTM　network,　thus　ensuring　that　the　reconstructed　signals　possess　combined　features　with　spatial　and　temporal　dependencies.　The　model＇s　discriminator　utilizes　PatchGAN,　employing　small　segments　of　the　signal　as　discriminative　inputs　to　concentrate　the　training　process　on　capturing　signal　details.　Evaluating　the　model　using　two　real　FECG　signal　databases,　namely　＂Abdominal　and　Direct　Fetal　ECG　Database＂　and　＂Fetal　Electrocardiograms,　Direct　and　Abdominal　with　Reference　Heartbeat　Annotations＂,　resulted　in　a　mean　MSE　and　MAE　of　0.019　and　0.006,　respectively.　It　detects　the　FQRS　compound　wave　with　a　sensitivity,　positive　predictive　value,　and　F1　of　99.51%,　99.57%,　and　99.54%,　respectively.　This　paper＇s　model　effectively　preserves　the　morphological　information　of　FECG　signals,　capturing　not　only　the　FQRS　compound　wave　but　also　the　fetal　P-wave,　T-wave,　P-R　interval,　and　ST　segment　information,　providing　clinicians　with　crucial　diagnostic　insights　and　a　scientific　foundation　for　developing　rational　treatment　protocols.