The lack of observations and biases of winds and bathymetry in the coastal region result in significant biases in the numerical modeling of sea waves. In this study, we establish a two-module bias correction model based on the Long-Short Term Memory neural network (denoted as TM-BCM) which accounts for the data imbalance by separately training the neural network for low and high sea states. The in-situ observations in 2020 and 2021 are used to model training and testing, respectively. The test results show that TM-BCM significantly reduces the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of modeled sea waves by 52.9% and 65.5%, respectively. In addition, TM-BCM outperforms the single-module model (denoted as SM-BCM) that does not account for the imbalance of training data. Our results highlight the great value of deep learning in correcting biases for numerical models and the importance of accounting for data imbalance in training the neural network.