At present, to address the challenges of feature fusion methods in the field of voiceprint recognition and the limited expressive capability of existing models, this paper proposes an enhanced Res2Net architecture and an improved Long Short-Term Memory (SLSTM) neural network. Furthermore, it integrates three features: MFCC, FBank, and LFBank. Initially, by fusing these three features, the characteristics of sound are comprehensively captured. Subsequently, in conjunction with the enhanced Res2Net, multiple feature representations at different scale combinations are obtained for each input feature through a more fine-grained operational mode. Finally, the extracted feature information is fed into the stacked long short-term memory neural network to handle sequence-related problems and improve the model's expressive power. Experimental results show that the proposed method performs well on the CN-Celeb dataset, with the equal error rate and the minimum detection cost function reaching 2.89% and 0.3725, which proves the robustness and accuracy of the proposed method.