Abstract: To explore the impact of climatic factors on Thunnus albacares catches, we studied its relationship with low-frequency climatic factors by using correlation analysis, BP neural network, LSTM model, BiLSTM model and CNN-BiLSTM model based on the data of nine climate factors, including Southern Oscillation Index (SOI), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), North Pacific Index (NPI), global sea-air temperature anomaly index (dT), El Niño-related indexes (Niño1+2, Niño3, Niño4, Niño3.4) from 1960 to 2021, as well as global T. albacares catches data. The results show that the importance of climate change characterization factors on T. albacares catches followed a descending order of dT＞SOI＞Niño1+2＞PDO＞NPI＞NAO, whose corresponding optimal lag periods were 0, 11, 6, 5, 15 and 0 years, respectively. CNN-BiLSTM model had the highest prediction accuracy, followed by BiLSTM, LSTM and BP. The goodness of fit between the predicted and actual values of CNN-BiLSTM model was 0.887, with a mean absolute error of 0.125 and a root mean square error of 0.154. The trend of predicted values and actual values was basically consistent, indicating a good model fitting effect.