[Objective]In　power　systems,　the　line　loss　rate　is　an　important　economic　and　technical　indicator　for　measuring　the　design,　operation,　maintenance,　and　management　levels　of　the　power　grid.　It　plays　a　significant　role　in　ensuring　the　stable　and　economical　operation　of　the　power　grid　and　improving　the　efficiency　of　power　supply.　However,　in　the　context　of　big　data　characterized　by　a　surge　in　the　number　of　users　and　diversified　energy　usage　characteristics,　the　management　of　line　loss　rates　faces　significant　challenges.　Traditional　line　loss　calculation　methods　rely　on　power　grid　parameters,　having　a　low　level　of　refinement　and　poor　calculation　accuracy.　[Methods]　In　response　to　these　issues,　this　paper　proposed　an　intelligent　analysis　method　for　line　loss　in　low-voltage　distribution　networks　based　on　an　improved　recurrent　neural　network　(RNN),　aiming　to　improve　the　accuracy　and　efficiency　of　line　loss　calculation　through　intelligent　means.　The　method　first　utilized　the　K-means　algorithm　to　classify　and　preprocess　massive　user　data　from　smart　distribution　networks,　thereby　reducing　data　redundancy.　Secondly,　the　analytic　hierarchy　process　(AHP)　was　employed　to　extract　line　loss　indicators　from　the　categorized　data,　which　were　then　input　into　a　deep　learning　model.　The　core　deep　learning　model　was　a　fusion　of　convolutional　neural　networks　(CNN)　and　an　improved　long　short-term　memory　(LSTM)　model,　capable　of　mining　distribution　network　data　features　to　achieve　intelligent　analysis　of　line　loss.　The　effectiveness　of　the　proposed　method　was　fully　demonstrated　through　validation　using　an　IEEE33-node　simulation　model.　[Results]　Experimental　results　indicate　that　the　mean　square　error　and　relative　error　percentage　of　the　proposed　method　are　3.　15　MW　and　2.　43%,　respectively,　demonstrating　high　computational　accuracy.　Compared　to　existing　methods,　the　proposed　method　has　a　distinct　advantage　in　intelligent　analysis　of　line　loss　in　distribution　networks　in　the　context　of　big　data,　capable　of　comprehensively　considering　various　factors　influencing　the　distribution　network　to　achieve　more　accurate　line　loss　calculation　results.　Moreover,　comparative　experiments　with　two　classical　methods　from　literature　further　validate　the　performance　advantages　of　the　proposed　method.　[Conclusion]　The　intelligent　analysis　method　for　line　loss　in　low-voltage　distribution　networks,　based　on　the　improved　RNN　model,　utilizes　the　K-means　algorithm　and　AHP　for　preprocessing　and　extracting　line　loss　indicators　and　then　employs　the　CNN-LSTM　model　for　in-depth　analysis,　effectively　enhancing　the　accuracy　and　efficiency　of　line　loss　calculations.　Although　this　method　is　primarily　aimed　at　line　loss　analysis　on　the　low-voltage　distribution　network　side　and　has　not　yet　been　deeply　studied　for　higher-voltage　line　loss　analysis,　it　shows　excellent　results　in　intelligent　analysis　of　line　loss　in　low-voltage　distribution　networks　and　has　practical　application　value.　Future　research　will　extend　to　broader　validation　analyses　to　improve　the　comprehensiveness　and　reliability　of　the　method.　Additionally,　the　development　of　this　method　also　provides　new　ideas　and　tools　for　further　research　and　application　of　smart　distribution　networks,　contributing　to　the　development　and　application　of　smart　grid　technology.　Employing　this　method　can　not　only　improve　the　accuracy　of　line　loss　calculations　but　also　provide　a　scientific　basis　for　the　optimized　management　of　the　power　grid,　which　is　of　significant　practical　importance　for　enhancing　the　operational　efficiency　of　the　power　grid　and　reducing　energy　loss.　With　the　continuous　advancement　of　technology　and　the　increasing　volume　of　data,　intelligent　line　loss　analysis　methods　will　become　an　indispensable　part　of　power　system　operation　and　maintenance.　©　2025　Shenyang　University　of　Technology.　All　rights　reserved.