Thunnus obesus is one of the main fish species for longline fishing in the Pacific. Aiming at the problems of most traditional forecast models, a new fishing ground forecast model based on empirical mode decomposition and two-way long and short-term memory neural network (EMD-BiLSTM) is proposed to realize a new production forecast method for fishery applications. First, the empirical mode decomposition mechanism (EMD) is used to decompose the catch per unit effort (CPUE) sequence to obtain decomposition components (IMF) of different scales. Then the influencing factors are combined to establish the two-way long and short-term memory neural network fishing ground forecast model (Bi-LSTM) respectively for the IMF components, so that the data processing advantages of the neural network can be fully utilized. Finally, the results are integrated as the final forecast value. The results show that compared with the Bi-LSTM model, the root mean square error and absolute error are reduced by 0.053 and 0.018, respectively; compared with the BP model, the root mean square error and absolute error are reduced by 0.208 and 0.048, respectively. Studies have shown that the EMD-BiLSTM model has a high forecast accuracy rate, which provides a new idea for related research on fishing ground forecast.