Presently, an electronic laryngoscope image is used to assess the severity of laryngopharyngeal reflux disease based on the reflux finding score (RFS) scale. This quantitative evaluation method increases the screening diagnosis objectivity. However, its misdiagnosis rate is high, and screening efficiency is moderate. An anti-flow-aided evaluation of the throat region based on RFS can be implemented using a deep learning algorithm. We propose a semantic segmentation algorithm for diagnosing laryngopharyngeal reflux disease based on existing knowledge of the RFS scale to segment the throat multi-region semantics in an electronic laryngoscope image. This algorithm resolves the problems of unbalanced sample categories and small target detection in the dataset used for this study. The intersection over union ratio for the dataset increased by 6.38 percentage points. Moreover, detection rates for small targets, such as voice-band groove, granuloma, and mucus, increased by 4 percentage points, 18 percentage points, and 75 percentage points, respectively. Furthermore, SE-ResNet and target area segmentation are used to quantify and evaluate the subjective items in the RFS scale. Thus, the auxiliary evaluation results aid in rapid and effective diagnosis of laryngopharyngeal reflux. The diagnostic accuracy of the proposed method is 94.40%. This study provides an innovative computer-aided assessment method for throat regurgitation that can be used for diagnostic reference based on the RFS scale. Hence, this study lays foundation for further research into throat regurgitation-related diseases.