Query-by-example (QbE) keyword spotting is convenient for users to define their own keywords, so it is useful in device control. However, conventional regular softmax, which is commonly used for training QbE models, has two limitations. First, the learned features are not discriminative enough. Second, norm variations of the unnormalized features affect computing cosine similarities. To address these issues, this paper introduces normalization and additive margin into residual networks for QbE keyword spotting. Features and weights are normalized on a hypersphere of fixed radius. Additive margin further helps to reduce the intra-class variations and increase inter-class differences. Based on public datasets AISHELL-1 and HelloNPU, we design three different test sets, namely in-vocabulary, out-of-vocabulary, and cross-corpus, to evaluate our proposed method. Experiments show that our proposed method can learn more discriminative embedding features. For totally unseen situation, our proposed method achieves a relative false rejection rate reduction of 46.60% when the false alarm rate is 2% in cross-corpus evaluation, compared with regular softmax.