Objective To explore the feasibility of deep learning technology for automatic recognition of fetal echocardiography images. Methods The detection model of YOLOv4 was improved and the multi residual hybrid attention module（MRHAM）was introduced，which was named MRHAM⁃YOLOv4⁃Slim. A total of 2000 fetal echocardiographic four chamber cardiac views at the Women and Children′ s Hospital，Qingdao University were selected to establish the experimental datasets. MRHAM⁃YOLOv4⁃Slim was compared with various artificial intelligence models for image recognition. Results MRHAM⁃YOLOv4⁃Slimaccuratelyidentified the cardiac cavity structure in the four chamber view. The accuracy rate was 0.85，the recall rate was 0.92，the F1 score was 0.88，and the average accuracy was 0.91. The accuracy of the model in identifying left atrium，right atrium，left ventricle

and right ventricle was 0.87，0.93，0.86 and 0.89 respectively. Conclusion The performance of MRHAM ⁃YO⁃LOv4⁃Slim model was better than most of the models，which identified theheartstructures in the four chamber view more accurately. Its recognition level was close to that of ultrasound doctors. This study would contribute to the further development of artificial intelligence in fetal echocardiography.