To address the limitations of traditional habitat prediction models in capturing the lagged effects of environmental factors with time series information on tuna spatial distribution, this study utilized tuna purse-seine fishing log data from 2021 to 2024. Long-short term memory （LSTM） neural network models were constructed with lag durations of 1 day, 5 days, 10 days, and 15 days to predict catch per unit of effort （CPUE） and geographic coordinates （latitude and longitude）.The findings indicate that the 10-day lag model exhibited the highest accuracy, with a mean square error （MSE） of 0.018 7 and a mean absolute error （MAE） of 0.077 6, suggesting that the spatial distribution of skipjack is influenced by cumulative short-term environmental effects. Validation of the optimal model demonstrated the R² of 0.97 for predicted versus actual latitude and 0.65 for longitude, indicating a strong alignment between predicted and observed spatial distributions.This research offers new insights into the dynamic mechanisms underlying skipjack tuna habitat characteristics and ecological processes. Furthermore, it provides critical references for the scientific management of skipjack purse seine fisheries in the Western and Central Pacific Ocean.