Objective To construct an automatic recognition and score quality evaluation system for facial screening in early pregnancy based on the YOLOv4 model. Methods The facial screening images during 11～14 gestational weeks were selected as the research objects, including 1001 for model construction set and 610 for clinical verification set. The model construction included a training set, a test set and a validation set, which were used to train and verify the classification and recognition of the AI model on the midsagittal plane and the retronasal triangle. Taking expert photographer classification as the standard, the consistency between AI and expert classification was compared and analyzed. Results The precision, Recall, and F1-score of the model for the key anatomical structures in the test set were all above 80%. The recognition performance of the model on the test set was extremely consistent with the expert classification, with MSP and RNT kappa values of 0.888 and 0.810, respectively. The model was in favorable agreement with the expert classification in the clinical validation set, with MSP and RNT kappa values of 0.781 and 0.690, respectively. The image grade evaluation of the clinical validation set by the model was in perfect agreement with the expert evaluation, and the MSP and RNT Kendall''s W were 0.760 and 0.789, respectively. Conclusions AI has a strong accuracy in the classification of views for first-trimester screening, and can be used as an auxiliary method for the training and image quality evaluation of primary and primary care doctors.