Multisource data analysis with privacy protection is a research hotspot in big data analysis. Learning classifiers from multiparty privacy data has important applications. We propose a twostage privacy protection analyzer model. Firstly, we use the PATET model with privacy protection to train the classifier for private data. Then we gather the multi-party classifier, and use transfer learning to transfer the set knowledge to the global classifier to establish an accurate global classifier with differential privacy. The global classifier does not need to access any party’s private data. Experimental results show that the global classifier can not only interpret each local classifier well, but also protect the details of the privacy training data of all parties.