面向电力营销的深度挖掘算法设计与应用

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中图分类号：TP301.6 文献标志码：A 文章编号：2095-2945(2026)03-0032-04

Abstract:Withtherapiddevelopmentofthecurrenteconomyandtechnology，theelectricityconsumptionbehaviorofpower usershasshownanunprecedenteddiversficationtrend,anddemand modelshave becomeincreasinglydiverseandchanging rapidly.Inordertoefectivelyrespondtothismarketchange，powercompaniesurgentlyneedtransformationandupgradingand adoptinovativemarketing strategies toaccuratelymatchuserneeds,improveservicequalityandenhancemarketcompetitivenes. Aimingatmanyproblemsexistingintraditional powermarketingmethods，suchasrigidstrategies,lowmatchingofuserneeds, unevendistributionofpowerresourcesandseriouswasteofresources，thispaperbuildsandproposesapersonalized recommendationalgoritmforpowermarketingbasedonbigdataanalysisandartficialinteligence(Al)technologyThealgoritm integratesadvancedmachineleaming(ML）modelsandachievesaccuratepredictionsofuserselectricityconsumptionhabits, preferencesandfuturenedsthroughin-depthminingofmasiveuserelectricityconsumptiondata.Experimentalresultsshow thatthepersonalizedrecommendationalgorithmproposed inthispaperperformswellinimprovingusersatisfactionandenhancing userstickinessandsignificantlyreducesoperatingcostsandresourcewasteofpowercompanies，providingstrongsupportforthe sustainable development of the power industry.

Keywords:powerarketing;prsonaliedrecommendationalgoritm;bigata;rificialintellgence(A);dsignandaplicatio

21世纪以来，随着中国经济社会的持续繁荣与科技的迅猛发展，社会各行业对电力的需求日益增长，用电用户数量呈现出爆发式上升趋势。（剩余7319字）