In this study, we forecast hourly relativistic (>2 MeV) electron fluxes at geostationary orbit for the next 72 hours using a deep learning model. For this we consider three deep learning methods, such as multilayer perceptron (MLP), LSTM, and sequence-to-sequence based on LSTM. The input data of the model are solar wind parameters (temperature, density and speed), interplanetary magnetic field (|B| and Bz), geomagnetic indices (Kp and Dst), and electron fluxes themselves. All input data are hourly averaged ones for the preceding 72 consecutive hours. We use electron flux data from GOES-15 and -16, and perform cross-calibration to match the two data. Total period of the data is from 2011 January to 2021 March (GOES-15 data for 2011-2017 and GOES-16 data for 2018-2021). We divide the data into training set (January-August), validation set (September), and test set (October-December) to consider the solar cycle effect. Our main results are as follows. First, the MLP model, which is the best, successfully predicts hourly electron fluxes for the next 72 hours. Second, root-mean-square error (RMSE) of our model is from 0.18 (for 1h prediction) to 0.68 (for 72h prediction), and prediction efficiency (PE) is from 0.97 to 0.53, which are much better than those of the previous studies. Third, our model well predicts both diurnal variation and sudden increases of electron fluxes associated with fast solar winds and interplanetary magnetic fields. Our study implies that the deep learning model can be applied to forecasting long-term sequential space weather events.