<p>Ship motion attitude is influenced by dynamic marine conditions, presenting significant challenges in developing effective prediction networks. Contemporary prediction networks demonstrate limitations in hidden feature extraction, long-term dependency maintenance, and frequency characteristic incorporation. This paper presents an enhanced model integrating the informer network with a Time Convolutional Network (TCN) and a Frequency-Enhanced Channel Attention Mechanism (FECAM). The model employs a TCN for multi-feature extraction and applies Dimension-Segment-Wise (DSW) embedding for comprehensive multi-dimensional sequence analysis. Furthermore, it incorporates discrete cosine transform within the FECAM module for thorough data frequency analysis. The model integrates these components with the informer model for multivariate prediction. This approach maintains the informer model’s capabilities in long-term multivariate prediction while enhancing feature extraction and local frequency information capture from ship motion attitude data, thus improving long-term multivariate prediction accuracy. Experimental results indicate that the proposed model outperforms traditional ship motion attitude prediction methods in forecasting future motion, reducing attitude prediction errors, and improving prediction accuracy.</p>