To meet the application needs of speaker recognition for short-duration speech in open scenarios, we herein optimize the speaker recognition model in aspects of accuracy and robustness.First, to realize the selection of important frequency features from the input acoustic data, a Reweighted-based Feature Enhancement Layer (RFEL) and a Reweighted-based Feature Enhancement Network (RFEN) are proposed to enhance the feature representation.Second, the loss function of Misclassified Vector guided Softmax loss (MVSoftmax) in face recognition is introduced into the speaker recognition to improve the mining ability towards hard samples.Third, a combined loss function of MVSoftmax and few-shot learning based Angular Prototypical loss (AP) is proposed, which solves the mismatch between the classification loss function and the actual evaluation requirements of speaker recognition, and relieve the strong dependence of the metric function on the sampling strategy.Finally, the experimental results show that the performance metric EER of the proposed model is reduced by 12.45% and the minDCF is decreased by 14.09% compared to the baseline model, achieving excellent performance in speaker recognition.