Aiming at the problem of channel mismatch in the field of voiceprint recognition and incomplete acquisition of voiceprint features under short speech or noise conditions,a method that combines traditional methods with deep learning is proposed, and the ResNet model is used as the student model to perform knowledge distillation on the I-Vector model as the teacher model. We construct a ResNet network based on metric learning, introduce an attentive statistics pooling layer, capture and emphasize the important information of voiceprint features, and improve the distinguishability of voiceprint features. The mean square error (MSE) is combined with the loss based on metric learning to reduce computational complexity and enhance model learning capabilities. Finally, the trained model is used for voiceprint recognition test, and compared with the voiceprint recognition model under a variety of deep learning methods. It's found that the equal error rate (EER) is reduced by at least 8%, and the equal error rate has reached 3.229%, indicating that the model can perform speaker verification more effectively.