In order to meet the accuracy requirements of hospitals for abnormal detection of fetal monitor data,a hospital fetal monitor data anomaly detection method based on Euclidean distance was proposed.In this method,according to the abnormal detection method of fetal monitor data in hospitals,the ultrasound Doppler detector was used to collect fetal heart rate signal data,and the fetal heart rate signal denoising method based on adaptive filtering was used to remove the noise components of the collected fetal heart rate signal.The denoised fetal heart rate signal was used as the identification sample of the abnormal fetal heart rate signal identification method based on Euclidean distance method.The Euclidean distance Euclidean distance between the denoised fetal heart rate signal and the normal fetal heart rate signal was analyzed by combining the Euclidean distance method and the strong classifier to analyze whether the virtual sinusoidal distance between the denoised fetal heart rate signal and the normal fetal heart rate signal was greater than the tuned Euclidean distance threshold.Fetal heart rate signal samples greater than the threshold were classified and diagnosed as abnormal monitoring data.The fetal heart rate information collected by the fetal monitor in the hospital was accurate,and the abnormal detection results were valid and reliable.