This paper conducts an in-depth study on the application of YOLO-v4, Faster R-CNN, and DenseBox neural networks for training deep learning models, using smart restaurants and dish recognition as examples. Unmanned settlement systems in smart restaurants can effectively reduce labor costs and improve operational efficiency. The core of an unmanned settlement system is the accurate identification of dishes. To address this issue, deep learning methods were used to recognize images from Chinese dish databases. YOLO-v4, Faster R-CNN, and DenseBox neural networks were selected for training deep learning models, and their recognition results were compared and analyzed. Experimental results show that the Faster R-CNN network model performs the best, with the ability to automatically extract image features, achieving a recognition rate of 95.4% for dishes, a recall rate of 82%, and an F1 value of 88.2%. This study provides a reliable foundation for the intelligent recognition of dishes and the application of smart catering.