Currently　voiceprint　Recognition　systems　are　playing　an　increasingly　important　role　in　social　life.　The　most　popular　voiceprint　Recognition　technology　currently　relies　on　neural　networks　to　extract　speaker＇s　features.　The　successful　ECAPA-TDNN　architecture　is　an　improved　time-delayed　neural　network　based　on　the　x-vector　architecture,　which　explicitly　models　channel　interdependencies　by　introducing　the　introduction　of　Squeeze　and　Excitation　blocks,　while　the　SEResNet　block　expands　the　frame　layer　context　by　adjusting　the　proportion　of　channels　with　global　properties　of　the　recording.　In　this　paper,　a　number　of　improvements　are　proposed　to　the　architecture　based　on　the　latest　trends　in　the　field　of　voiceprint　Recognition-related　issues.　Firstly,　the　initial　frame　layer　can　be　reset　as　a　one-dimensional　Res2Net　module　for　skip　connections.　In　consideration　of　the　effect　of　the　input　data　on　the　neural　network,　we　pre-process　the　input　audio　data　using　the　Melspectrogram　approach.　Secondly,　a　gated　recurrent　unit　(GRU)　network　is　introduced　and　embedded　in　the　residual　structure　of　the　multilayer　aggregation　feature　to　further　mine　the　temporal　contextual　information　in　the　audio　signal.　The　output　of　the　GRU　is　aggregated　with　the　output　of　the　last　SE-Res2Block　module　for　subsequent　feature　extraction.　Finally,　the　proposed　ArcFace　loss　function　is　used　in　this　paper　to　penalise　the　angle　between　the　depth　features　and　their　corresponding　weights　in　an　additive　manner,　thus　enhancing　both　intra-class　tightness　and　inter-class　variance　and　increasing　the　validation　of　our　proposed　model　architecture　for　voiceprint　Recognition.　©　2023　IEEE.