为解决传统统计学方法和机器学习法无法实现船舶变频海水冷却系统预期状态参数预测的问题，提出一种基于双向门控循环单元（Bidirectional Gated Recurrent Unit, BiGRU）的时序预测模型，采用数据归一化法处理高跨度数量级的特征数据，并结合敏感性分析法实现隐含层神经元的参数调优。以MATLAB Simulink仿真平台生成的变频海水冷却系统参数数据集作为特征样本数据进行训练，采用均方误差（Mean-Square Error, MSE）、校正决定系数（Adjusted Coefficient of Determination，Adjusted R²）评价指标评估模型预测性能，并建立循环神经网络（Recurrent Neural Network, RNN）和单向门控循环单元（Gated Recurrent Unit, GRU）模型进行对比,分析不同时序数据预测算法模型的鲁棒性。结果表明：基于参数调优的BiGRU模型对比RNN模型，MSE值至少降低73.13%，Adjusted R²值至少提高6.44%；对比GRU模型，MSE值至少降低67.86%，Adjusted R²值至少提高3.35%。BiGRU模型相比GRU与RNN模型可更为准确地预测系统预期状态参数，拥有优良的预测精度与稳定性，可为船舶安全评估体系提供准确的数据支撑，对船舶变频海水冷却系统参数预测具有参考价值。

In order to solve the problem that the traditional statistical methods and machine learning methods cannot predict the expected state parameters of marine variable frequency seawater cooling system, a time series prediction model based on bidirectional gated recurrent unit (BiGRU) was proposed, and the data normalization method was used to process the characteristic data of high span order of magnitude to realize the parameter optimization of hidden layer neurons by combining with sensitivity analysis method. Taking the parameter data set of variable frequency seawater cooling system generated by MATLAB Simulink simulation platform as the characteristic sample data for training, the evaluation indexes of mean square error (MSE) and adjusted coefficient of determination (Adjusted R²) were used to evaluate the prediction performance of the model, and the recurrent neural network (RNN) and unidirectional gated recurrent unit (GRU) models were established for comparison to analyze the robustness of different time series data prediction algorithm models. The results show that compared with RNN model, the value of MSE of BiGRU model based on parameter optimization is reduced by at least 73.13%, and the value of Adjusted R² is increased by at least 6.44%; compared with GRU model, MSE value decreases by at least 67.86%, and Adjusted R² value increases by at least 3.35%. Compared with GRU and RNN models, BiGRU model can more accurately predict the expected state parameters of the system with excellent prediction accuracy and stability, which can provide accurate data support for the ship safety assessment system, and has reference value for the parameter prediction of ship variable frequency seawater cooling system.