The　inherent　concept　drift　characteristics　of　industrial　process　data　leads　to　the　deterioration　of　the　soft　sensor　model＇s　performance.　Thus,　the　first　problem　is　to　identify　drift　samples　to　effectively　update　the　model.　Aiming　at　these　problems,　a　double-window　concept　drift　detection　method　oriented　to　the　modeling　of　difficult-to-measure　parameters　of　industrial　processes　is　proposed.　First,　support　vector　regression　is　used　in　the　outlier　sample　detection　window　to　obtain　the　outlier　samples　contained　in　the　real-time　process　data.　Then,　the　Euclidean　distance　between　the　outlier　sample　and　the　historical　sample　set　is　calculated　in　the　distribution　detection　window;　Next,　a　test　drift　index　combined　with　a　variety　of　distribution　test　methods　that　can　characterize　the　distribution　changes　contained　in　outlier　samples　is　defined,　so　as　to　realize　effective　identification　of　drift　samples.　Finally,　synthetic　and　real　industrial　process　data　sets　are　used　to　verify　the　effectiveness　of　the　proposed　method,　which　shows　better　performance　than　existing　methods.　©　2021,　Editorial　Department　of　Control　Theory　&　Applications　South　China　University　of　Technology.　All　right　reserved.