Accurate prediction of anomalous wave heights caused by tropical cyclones (TC) is extremely significant for coastal safety and offshore operations. Previous studies have barely considered TC wave height data which are characterized by short, nonlinear and non-stationary data, in this paper, an adaptive time–frequency decomposition method and a mixture of deep learning methods are used to develop a real-time TC wave height forecasting system (ATDNNS). Specifically, based on wave height data obtained from distributed sensors offshore China, the system uses ensemble empirical mode decomposition (EEMD) to decompose wave height data into multiple subseries containing local features of the original signal at different time scales, and these smoothed subseries are used as inputs to a long short-term memory network (LSTM) to reconstruct all outputs to provide accurate TC wave height forecasts. This study involves 28 TC events in the northwest Pacific Ocean with data collected from 14 coastal buoys over 9 years. The experimental results show that ATDNNS significantly outperforms the baseline model and previous studies. By comparing the results with the operational results of the state-of-the-art operational numerical models, it is shown that the system has the potential to fill the gap of poor initial forecast performance of numerical models, and we further check the effectiveness of ATDNNS for wave heights caused by the recent super typhoon Kong-Ray, achieving excellent results.