In recent years, Transfer Learning makes a great breakthrough in the field of machine learning, and the use of transfer learning technology in Cervical Histopathology Image Classification (CHIC) becomes a new research domain. In this paper, we propose an Ensembled Transfer Learning (ETL) framework to classify well, moderately and poorly differentiated cervical histopathology images. In this ETL framework, Inception-V3 and VGG-16 based transfer learning structures are first built up. Then, a fine-tuning approach is applied to extract effective deep learning features from these two structures. Finally, a late fusion based ensemble learning strategy is designed for the final classification. In the experiment, a practical dataset with 100 VEGF stained cervical histopathology images is applied to test the proposed ETL method in the CHIC task, and an average accuracy of 80% is achieved.