This paper presents a deep neural architecture which applies the siamese convolutional neural network sharing model parameters for learning a semantic similarity metric between two sentences. In addition, two different similarity metrics (i.e., the Cosine Similarity and Manhattan similarity) are compared based on this architecture. Our experiments in binary similarity classification for Chinese sentence pairs show that the proposed siamese convolutional architecture with Manhattan similarity outperforms the baselines (i.e., the siamese Long Short-Term Memory architecture and the siamese Bidirectional Long Short-Term Memory architecture) by 8.7 points in accuracy.