[Objective] This paper presents a new method to recognize metaphor, from the Chinese and English datasets. [Methods] First, we mapped the experimental dataset to vector space, which was also input to a convolutional neural network along with the property and keyword features. Then, we extracted the needed features with the help of convolutional and pooled layers, as well as classified them using SVM. Finally, we combined the Max-Pooling and Mean-Pooling to improve the extracted features’ accuracy. [Results] Compared with the traditional models, our method increased the accuracy of extracted features from the corpus of English verb-object, English adjective-noun and Chinese metaphor by 4.12%, 0.84% and 4.50% respectively. [Limitations] The Chinese word segmentation affects the training of word vector model. We need to add more layers to the convolutional neural networks. [Conclusions] The proposed method could effectively identify metaphor from Chinese and English corpus.
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