The use of a few examples for each class to train a predictive model that can be generalizedto novel classes is a crucial and valuable research direction in artificial intelligence. Thiswork addresses this problem by proposing a few-shot learning (FSL) algorithm called proxynetwork under the architecture of meta-learning. Metric-learning based approaches assumethat the data points within the same class should be close, whereas the data points inthe different classes should be separated as far as possible in the embedding space. Weconclude that the success of metric-learning based approaches lies in the data embedding,the representative of each class, and the distance metric. In this work, we propose asimple but effective end-to-end model that directly learns proxies for class representativeand distance metric from data simultaneously. We conduct experiments on CUB andmini-ImageNet datasets in 1-shot-5-way and 5-shot-5-way scenarios, and the experimentalresults demonstrate the superiority of our proposed method over state-of-the-art methods.Besides, we provide a detailed analysis of our proposed method.