Abstract: Machine translation quality estimation is an important task in natural language processing. Unlike the traditional automatic evaluation of machine translation, the quality estimation evaluates the quality of machine translation without human reference. Nowadays, the feature extraction approaches of sentence-level quality estimation depend heavily on linguistic analysis, which leads to the lack of generalization ability and restricts the system performance of the subsequent support vector regression algorithm. In order to solve this problem, we extract sentence embedding features using context-based word prediction model and matrix decomposition model in deep learning, and enrich the features with recurrent neural network language model feature to further improve the correlation between the automatic quality estimation approach and human judgments. The experimental results on the datasets of WMT’15 and WMT’16 machine translation quality estimation subtasks show that the system performance of extracting the sentence embedding features by the context-based word prediction model is better than the traditional QuEst method and the approach that extracts sentence embedding features by the continuous space language model, which reveals that the proposed feature extraction approach can significantly improve the system performance of machine translation quality estimation without linguistic analysis.