The expanding volume of HTTPS traffic (both legitimate and malicious) creates even more challenges for mobile network security and management. In this work, we propose AIBMF(Application Identification Based on Multi-view Features), a fine-grained approach to classify HTTPS traffic by their application type. The key idea of AIBMF is to combine three kinds of features-payload convolution features, packet size sequence and packet content type sequence. Based on these different view features, a deep learning model (using CNN, embedding and RNN) is constructed for HTTPS traffic identification task. To evaluate the effectiveness of AIBMF, we run a comprehensive set of experiments on a real-world dataset (about 100,000+ flows), which shows that our approach achieves 96.06% accuracy and outperforms the state-of-the-art method (3.6% on F1 score).