The extremely short-term prediction of seaplane motion can provide the rocking motion posture over the next few seconds, which is considered essential for ensuring safety during take-off and landing phases under adverse wind and wave conditions. Although some research has been conducted on extremely short-term prediction methods for seaplane motion, limited attention has been given to analyzing differences in the applicability of various methods. In this context, the NACA TN 2929 aircraft was taken as an example, and the three degree of freedom motion simulation data under typical working conditions were calculated based on potential flow theory. To compare the forecasting performance under different forecasting conditions, three typical extremely short-term prediction of seaplane motion models, namely AR (auto-regressive), LSTM (long short term memory), and TCN (temporal convolutional network), were constructed. The results show that compared to the AR model, the LSTM and TCN neural network models exhibit superior forecasting accuracy for longer prediction durations, effectively enabling accurate predictions of the heave, roll, and pitch motions of the seaplane at the ten-second level, providing a valuable theoretical reference for the selection of seaplane motion prediction algorithms.