Along　with　the　rapid　development　of　communication　network　construction,　the　operation　energy　consumption　grows　significantly　in　recent　years,　and　the　expensive　electricity　cost　is　hard　to　he　ignored.　Therefore,　it　is　necessary　to　develop　an　operation　energy　anomaly　detection　mechanism　to　enhance　the　control　ability　of　electricity　cost.　According　to　the　practical　distribution　and　data　characteristic　of　smart　meters,　this　paper　presents　a　distributed　anomaly　detection　method　of　operation　energy　consumption　based　on　deep　learning　methods.　An　IOT-based　distributed　structure　is　implemented　to　execute　data　interaction.　Stacked　sparse　autoencoder　is　used　to　extract　the　high-level　representation　from　massive　monitoring　data　acquired　automatically　from　actual　smart　meter　network.　Then　softmax　is　used　for　classification　to　detect　anomaly　and　send　alarm　messages　using　web　technologies.　The　experimental　results　show　that　the　proposed　method　with　good　prospect　for　intelligent　applications　achieves　better　accuracy　and　meanwhile　decreases　computing　delay　caused　by　central　arithmetic　method.