To　enhance　the　accuracy　of　theft　detection　for　electricity　consumers,　this　paper　introduces　a　novel　strategy　based　on　the　fusion　of　the　dual-time　feature　and　deep　learning　methods.　Initially,　considering　electricity-consumption　features　at　dual　temporal　scales,　the　paper　employs　temporal　convolutional　networks　(TCN)　with　a　long　short-term　memory　(LSTM)　multi-level　feature　extraction　module　(LSTM-TCN)　and　deep　convolutional　neural　network　(DCNN)　to　parallelly　extract　features　at　these　scales.　Subsequently,　the　extracted　features　are　coupled　and　input　into　a　fully　connected　(FC)　layer　for　classification,　enabling　the　precise　detection　of　theft　users.　To　validate　the　method＇s　effectiveness,　real　electricity-consumption　data　from　the　State　Grid　Corporation　of　China　(SGCC)　is　used　for　testing.　The　experimental　results　demonstrate　that　the　proposed　method　achieves　a　remarkable　detection　accuracy　of　up　to　94.7%　during　testing,　showcasing　excellent　performance　across　various　evaluation　metrics.　Specifically,　it　attained　values　of　0.932,　0.964,　0.948,　and　0.986　for　precision,　recall,　F1　score,　and　AUC,　respectively.　Additionally,　the　paper　conducts　a　comparative　analysis　with　mainstream　theft　identification　approaches.　In　the　comparison　of　training　processes,　the　proposed　method　exhibits　significant　advantages　in　terms　of　identification　accuracy　and　fitting　degree.　Moreover,　with　adjustments　to　the　training　set　proportions,　the　proposed　method　shows　minimal　impact,　indicating　robustness.