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Few-shot intent detection is a problem that only a few annotated examples are available for unseen intents, and deep models could suffer from the overfitting problem because of scarce data. Existing state-of-the-art few-shot model, Prototypical Network (PN), mainly focus on computing the similarity between examples in a metric space by leveraging sentence-level instance representations. However, sentence-level representations may incorporate highly noisy signals from unrelated words which leads to performance degradation. In this paper, we propose Semantic Transportation Prototypical Network (STPN) to alleviate this issue. Different from the original PN, our approach takes word-level representation as input and uses a new distance metric to obtain better sample matching result. And we reformulate the few-shot classification task into an instance of optimal matching, in which the key word semantic information between examples are expected to be matched and the matching cost is treated as similarity. Specifically, we design Mutual-Semantic mechanism to generate word semantic information, which could reduce the unrelated word noise and enrich key word information. Then, Earth Mover’s Distance (EMD) is applied to find an optimal matching solution. Comprehensive experiments on two benchmark datasets are conducted to validate the effectiveness and generalization of our proposed model.