In　the　thermal　comfort　field　survey,according　to　the　characteristics　of　the　six　factors　that　affect　human　thermal　comfort　and　the　human　subjective　evaluation,　this　paper　proposed　a　complete　set　of　outlier　detection　methods　innovatively.　First,　the　six　influential　factors　namely　indoor　temperature,　relative　humidity,　globe　temperature,　air　velocity,　metabolic　rate　and　clothing　thermal　resistance　were　cleaned.　Then,　based　on　the　six　factors　and　taking　the　standard　effective　temperature　as　the　reference　index,　the　d-based　K-nearest　Neighbor　(KNN)　classification　combined　with　Gaussian　distribution　was　adopted　to　deal　with　the　anomalous　value　of　TSV　(thermal　sensation　vote).　This　method　is　an　improvement　on　the　existing　research,　which　can　detect　the　outliers　in　the　data　more　comprehensively　and　objectively,　so　as　to　ensure　the　accuracy　of　subsequent　data　mining.　©　2020　16th　Conference　of　the　International　Society　of　Indoor　Air　Quality　and　Climate:　Creative　and　Smart　Solutions　for　Better　Built　Environments,　Indoor　Air　2020.　All　rights　reserved.