The marine environment is complex and changeable, ship navigation is easily affected by factors such as wind, waves, ocean currents and other factors, ship motion is characterized by nonlinearity and coupling. Aiming at the problem that traditional ship motion prediction methods have insufficient efficiency in extracting time series data and are difficult to achieve high-precision prediction results, a sample convolution and interaction - channel attention (SCI-CA) neural network ship pitch motion prediction model is proposed. The model uses multi-category ship motion attitude data as input, splits the input into two subsequences, utilizes the recursive down sampling convolution interaction structure of the sample convolution interaction network (SCI), and combines the rich features aggregated from multiple resolutions to improve the utilization of deep features of ship motion data. Then, channel attention mechanism (CA) is used to improve the weight ratio of effective channels, and the residual structure is input to the full connection layer to obtain the final prediction result. The simulation results of real ship data show that the prediction accuracy of the SCI-CA combined model is higher than that of other models, and its mean absolute percentage error (MAPE) and root mean square error (RMSE) are significantly reduced, verifying the effectiveness of the SCI-CA model in predicting ship motion.