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.