Abstract: To ensure the safe operation of the nuclear reactor system in the ocean environment, it is necessary to establish a set of computational models to obtain the real-time liquid level in the pressurizer. Therefore, this paper establishes an experimental system to collect relevant data, optimizes the LSTM neural network through the sparrow search algorithm, and establishes a regression prediction model between the measured pressure, motion attitude parameters and liquid level. The results show that the neural network model established in this paper has excellent prediction accuracy, which is significantly better than other traditional neural networks. The model has good generalization ability, and the prediction accuracy of fresh samples is still acceptable. Integrated into the control system, the liquid level can be output in real time, which can improve the safety of nuclear power operation under ocean conditions and provide reference for the intelligent operation and maintenance of nuclear power.