Most of the existing cross-domain sentiment classification methods only take advantage of the migration feature from a single source domain to a target domain， without fully considering connections between target domain instances and different source domains. To solve this problem， this paper proposes an unsupervised multiple-source cross-domain sentiment classification model. First， the base classifier is trained by using the migration feature of a single source domain to a target domain， and different base classifiers are weighted. Then， the ensemble consistency of different base classifiers on the target domain instance prediction is taken as the objective function， and the objective function is optimized to obtain the weights of different base classifiers. Finally， the weighted base classifier is used to obtain the sentiment classification results of the target domain. The model is tested on Amazon's product review data set and Skytrax data set， and is compared with six baseline models. Experimental results show that compared with the baseline model， the classification performance of the proposed method is significantly improved in eight different target domains.