Abstract : Objective To design an automated detection protocol for morphological detection of sperm head based on deep learning and evaluate its efficiency, accuracy and reliability. Methods Fourteen pictures for each of 1 000 samples were analyzed by using the pretrained Y0LO target detection model and VGG16 classification model. At least 200 sperm were detected for each sample. Equal amount of samples were analyzed by manual microscope examination, and the efficiency，accuracy and correlation between the two methods were compared. Results The morphology of sperm heads which were manually classified but untrained was detected by pre-trained classification model ，and the prediction accuracy reached to 95. 5%. The detection time for clinical each sample was only 10 seconds ,and its accuracy and efficiency were higher than those of manual microscope ex amination. The percentages of the sperms with normal morphology were significantly positively corelated (r=0.84, P<0.01) in the detections of both the methods. Conclusion The detection protocol proposed in this study can greatly improve work fficiency, and its reliability and accuracy exceed those of manual microscope examination.