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Computing sentiment strength of product reviews is a challenge in the field of sentiment analysis. The results obtained by standard condition random fields (CRFs) in the previous work were not well, so cascaded CRFs model is used to compute the strength of sentence sentiment in Chinese in this paper. The present approach contains two layers: in the first layer CRFs model is used to judge the polarity of the sentence, negative, positive or objective, then strength of polarity is obtained by CRFs model in the second layer. The proposed framework facilitates mapping the product reviews into five classes-(i.e. strong positive, general positive, objective, general negative and strong negative) by considering the label redundancy among layers. The authors choose context, conjunction, evaluated-word and the results of the polarity classification as the features of cascaded CRFs model. Experiments on the task3 corpus of COAE2008 show the remarkable performance of the proposed approach. Results of the first layer are added to the features in the second layers which can have the advantage to compute the sentiment strength of sentences.