In order to overcome the deficiency of sentiment analysis based on traditional machine learning, which difficulty of effective feature selection and inadequacy of marked training corpus will affect the performance of the classification system, we address the sentiment emotions analysis problem of Chinese product reviews text by combining convolutional neural network (CNN) with bidirectional long-short term memory network (BiLSTM) in this paper. The CNN can extract the sequence features from the global information, and it is able to consider the relationship among these features. The BiLSTM not only solves the long-term dependency problem, but also considers the context of the text at the same time. The result of numerical experiments shows that the proposed model achieves better metrics performance than the state-of-the-art methods.