This paper first explores and evaluates English-Chinese translation skills through deep learning in machine learning based on big data technology, focusing on applying convolutional neural networks and long and short-term memory network models in English-Chinese translation. Then it is to construct a deep learning evaluation model based on the dataset with text-level labels of English-Chinese translation. The structure of the deep learning evaluation model consists of three categories: data representation of text, feature extraction of text, and text classifier. Finally, the research object is determined from the purpose of the study, and the data analysis is performed on the experimental and control groups using the deep learning model to characterize the fluency by the continuous convergence of the data. The results showed that the continuous convergence of the experimental group remained in the range of 76.95% to 82.6%, and its average value was 79.76%. The continuous convergence of the control group remained in the range of 60.15% to 71.92%, with a mean value of 67.02%. The average convergence value of the experimental group was 12.74% higher than that of the control group, and the experimental group outperformed the control group. This study should learn and cultivate English-Chinese translation skills while improving the efficiency and accuracy of English-Chinese translation, which is a guiding reference value for the progress of translation talents.