Abstract: In autumn, the Indian Ocean Dipole (IOD) has the strongest interannual variability in the tropical Indian Ocean. It will influence the climate in many parts of the world due to atmospheric teleconnection. The current coupled climate model has very limited IOD forecasting skills, which are far inferior to those of El Niño events in the tropical Pacific. The authors used the convolutional neural network (CNN) of the deep learning and the multi-layer perceptron (MLP) of the artificial neural network, respectively, to perform IOD prediction due to the super capability of deep learning in processing data. In order to explore the forecasting capabilities of CNN, this article only uses three initial conditions in the boreal spring with low prediction skill to forecast the Indian Ocean Dipole Mode Index (DMI), East Pole Index (EIOI), and West Pole Index (WIOI) for the next seven months. The results show that the CNN model can make useful predictions for the DMI, EIOI, and WIOI at least six months in advance. When compared with the current state-of-the-art general coupled model, the CNN model significantly improves the prediction skills of the DMI and EIOI while only slightly improving WIOI prediction skills. The CNN model could predict the strong IOD events in 1994, 1997, and 2019 well for the lead time longer than seven months. In general, CNN outperforms the traditional neural network MLP for the IOD prediction due to its strong capability to capture the spatial structure characteristics of the Indian Ocean SST.