In　clinical　practice,　obstetricians　use　visual　interpretation　of　fetal　heart　rate　(FHR)　to　diagnose　fetal　conditions,　but　inconsistencies　among　interpretations　can　hinder　accuracy.　This　study　introduces　MTU-Net3+,　a　deep　learning　model　designed　for　automated,　multi-task　FHR　analysis,　aiming　to　improve　diagnostic　accuracy　and　efficiency.　The　proposed　MTU-Net3　+　was　built　upon　the　UNet3　+　architecture,　incorporating　an　encoder,　a　decoder,　full-scale　skip　connections,　and　a　deep　supervision　module,　and　further　integrates　a　self-attention　mechanism　and　bidirectional　Long　Short-Term　Memory　layers　to　enhance　its　performance.　The　MTU-Net3　+　model　accepts　the　preprocessed　20-minute　FHR　signals　as　input,　outputting　categorical　probabilities　and　baseline　values　for　each　time　point.　The　proposed　MTU-Net3　+　model　was　trained　on　a　subset　of　a　public　database,　and　was　tested　on　the　remaining　data　of　the　public　database　and　a　private　database.　In　the　remaining　public　datasets,　this　model　achieved　F1　scores　of　84.21%　for　deceleration　(F1.Dec)　and　61.33%　for　acceleration　(F1.Acc),　with　a　Root　Mean　Square　Baseline　Difference　(RMSD.BL)　of　3.46　bpm,　0%　of　points　with　an　absolute　difference　exceeding　15　bpm(D15bpm),　a　Synthetic　Inconsistency　Coefficient　(SI)　of　44.82%,　and　a　Morphological　Analysis　Discordance　Index　(MADI)　of　7.00%.　On　the　private　dataset,　the　model　recorded　an　RMSD.BL　of　1.37　bpm,　0%　D15bpm,　F1.Dec　of　100%,　F1.Acc　of　87.50%,　an　SI　of　12.20%　and　a　MADI　of　2.79%.　The　MTU-Net3　+　model　proposed　in　this　study　performed　well　in　automated　FHR　analysis,　demonstrating　its　potential　as　an　effective　tool　in　the　field　of　fetal　health　assessment.