Tibetan word segmentation and POS tagging are the primary tasks of Tibetan natural language processing. Most of existing methods of Tibetan word segmentation and POS tagging are based on rules and statistics, which need manual construction of features. In addition, the joint mode has shown stronger capabilities for word segmentation and POS tagging, and have received great interests. In this paper, we propose Bi-LSTM+IDCNN+CRF structures, a simple yet effective end-to-end neural network model, for joint Tibetan word segmentation and POS tagging. We conduct step-by-step and joint experiments on the Tibetan datasets. The results demonstrate that the performance of the Bi-LSTM+IDCNN+CRF model is the best regardless of the step-by-step or joint mode. We obtain state-of-the-art performance in the joint tagging mode. The F1 score of the word segmentation task reached 92.31%, and the F1 score of the POS tagging task reached 81.26%.