The existing Chinese-English machine translation has problems such as inaccurate word translation and difficult translation of long sentences. To this end, this paper proposes a new machine translation model based on bidirectional Chinese-English translation incorporating translation knowledge and transfer learning, and the components of this model include a recurrent neural network-based translation quality assessment model and a self-focused network-based model. The experimental results demonstrate that our method works better on the dataset of machine translation quality assessment task for Chinese-English translation with more information, and the Pearson correlation coefficient of its quality assessment feature vector (such as word prediction vector representation) is higher.