Pollution　source　enterprises　are　numerous　and　widespread.　The　production　and　pollution　treatment　processes　of　each　enterprise　vary,　a　lack　of　effective　and　uniform　regulatory　indicators　and　early　warning　systems　are　concerning.　This　creates　problems,　such　as　difficult　supervision,　poor　real-time　performance,　and　a　large　workload.　This　study　proposes　a　method　for　identifying　the　environmental　anomalies　of　enterprises　based　on　electricity　data　mining.　First,　K-means　clustering　is　used　to　identify　the　operating　status　of　the　equipment,　and　a　model　of　the　enterprise　production　line　is　constructed　based　on　dynamic　time-warping　distance.　Next,　continuous　and　intermittent　production　lines　are　classified　based　on　historical　data　statistics.　Furthermore,　the　Fourier　transform　is　used　to　identify　the　production　cycle　of　the　production　line　to　establish　a　model　of　the　environmental　conditions　suitable　for　the　enterprise.　Subsequently,　the　environmental　condition　identification　method　is　proposed　to　identify　the　environmental　conditions　for　continuous　and　intermittent　production　lines.　Finally,　the　proposed　method　is　validated　using　the　monitoring　data　of　actual　pollution　source　enterprises.　The　electric　power　intelligent　environmental　protection　platform　developed　based　on　the　proposed　method　has　been　implemented　in　certain　provinces,　achieving　suitable　results.　This　platform　enables　the　environmental　protection　department　to　grasp　the　situation　of　enterprise　environmental　protection,　providing　both　technical　means　and　data　support.　©　2025,　State　Power　Economic　Research　Institute.　All　rights　reserved.