Focusing on the issue that most of existing approaches need sentiment lexicon annotated manually to extract sentiment features, a sentiment analysis method of product reviews based on Contrastive Divergence-Restricted Boltzmann Machine (CD-RBM) deep learning was proposed. Firstly, product reviews were preprocessed and represented as vectors using the bag-of-words. Secondly, CD-RBM was used to extract the sentiment features from product review vectors. Finally, the sentiment features were classified with Support Vector Machine (SVM) as the sentiment analysis result. Without any manually pre-defined sentiment lexicon, CD-RBM can automatically obtain the sentiment features of higher semantic relevance; combining with SVM, the correctness of the sentiment analysis result is guaranteed. The optimum training period of RBM was experimentally determined as 10. In the comparison experiments with methods including RBM, SVM, PCA+SVM and RBM+SVM, the combination method of CD-RBM feature extraction and SVM classification shows the best precision and best F-measure, as well as better recall.