Since the paraphrase extracted from the general domain tends to cause paraphrase match deviation in the specific-domain automatic evaluation of machine translation, this paper proposes an approach exploited specific-domain paraphrase related to the test set to enhance automatic evaluation of machine translation. First, the K-means algorithm is utilized to cluster general-domain monolingual corpus, and the specific-domain training data via improved M-L approach is obtained. Then, the specific-domain paraphrase table is extracted from the training data by Markov network model. Finally, the extracted paraphrase table is applied to automatic MT evaluation metrics to improve word match. The experimental results on the dataset of WMT’14 Metrics task and WMT’15 Metrics task show that the METEOR metric and the TER metric using the specific-domain paraphrase table yield better performance than that using the general-domain paraphrase table.