In　view　of　the　influence　of　industrial　users′　industry　attributes　on　their　power　consumption　patterns,　a　power　consumption　anomaly　identification　method　considering　industry　relevance　is　proposed　in　this　article.　Based　on　the　real　industrial　consumer　power　consumption　data,　the　typical　load　characteristic　curves　of　each　industry　are　generated,　and　the　improved　grey　correlation　degree　algorithm　is　used　to　calculate　the　relevance　between　the　power　consumption　characteristics　of　power　users　and　the　typical　power　consumption　characteristics　of　the　industry.　In　this　way,　the　industry　relevance　characteristics　of　users　are　achieved.　The　multi-head　attention　(MHA)　is　used　to　extract　the　features　contained　in　load　sequences.　Combined　with　the　industry　relevance　features,　the　reconstruction　error　provided　by　the　variational　autoencoder　(VAE)　is　used　as　the　anomaly　decision　metric　to　formulate　the　MHA-VAE　depth　anomaly　detection　model　to　identify　various　types　of　industrial　users′　power　consumption　anomalies.　Results　show　that,　the　accuracy,　detection　rate　and　false　detection　rate　after　introducing　users′　industry　relevance　are　96.84%,　98.02%,　and　4.35%,　respectively.　Compared　with　only　considering　the　load　characteristics　of　users,　the　accuracy　is　increased　by　1.06%　and　the　error　detection　rate　is　reduced　by　2.24%.　©　2023　Science　Press.　All　rights　reserved.