In　light　of　the　continuous　change　of　the　state　and　the　drift　of　performance　of　complex　electromechanical　systems　in　process　industry,　a　dynamic　state　tagging　method　based　on　self-organizing　feature　map　network　is　proposed.　First,　a　multi-variable　coupling　relationship　network　is　construct　and　the　characteristics　are　extracted.Second,　thedynamic　tagging　process　is　divided　into　two　stages　where　states　are　updated　passively　and　actively　respectively.　During　the　former　process,　the　self-organizing　feature　map　network　is　constantly　trained　to　adapt　to　new　situations　and　drift　of　performance.　During　the　later　process,　the　influence　of　the　system＇s　historical　states　on　the　network　model　is　eliminated.　Finally,　the　proposed　method　is　verified　effective　byanalyzing　the　data　of　the　real　chemical　production　system.　Experimental　results　show　that　this　method　is　able　to　mark　the　constantly　changing　multiple　states　during　the　operation　of　complex　electromechanical　systems.　It　establishes　a　state　marking　knowledge　base　in　agreement　with　the　dynamic　evolution　process　of　the　system,　providing　a　basis　for　the　identification　and　prediction　of　system　state.　©　2020,　Editorial　Department　of　JVMD.　All　right　reserved.