In order to solve the problems of low prediction accuracy and difficulty in Model construction during the application of most prediction models in the field of intelligent operation and maintenance in ships, a combined prediction model of ship system and equipment state parameters, ARIMA-KF model, which combines autoregressive integral moving average model(Auto-Regressive Integrated Moving-Average Model,ARIMA) and Kalman filter(KF), is proposed. Firstly, the single-step and multi-step prediction models of ARIMA are constructed. Secondly, the Kalman filter algorithm is used to optimize the parameter values of the ARIMA prediction model, and the ARIMA-KF combined prediction model is obtained. Finally, the state parameters of Marine seawater cooling system are predicted based on the combined model, and the predicted values are compared with the actual values obtained by the actual ship and the error analysis is made. The results show that the prediction accuracy of the combined ARIMA-KF model is about 3% higher than that of the single ARIMA model.The results of this study have certain guiding significance for the health management and condition-based maintenance of ship system and equipment.