Nuclear power plants have gradually increasing demand for the use of plate heat exchangers. Existing fouling thermal resistance prediction models have low generalization capabilities and few design options from the time series angle. Through the principal component analysis of the experimental data of the RRI/SEC heat exchanger of Unit 1 of Ling'ao Nuclear Power Plant, the long- and short-term memory neural network design model was optimized to predict the instantaneous fouling thermal resistance, covering variables such as the temperature of 12 pipelines and the flow rate of 4 pipelines. The model can accurately predict the demand for dirt cleaning in the next 25 days with an accuracy of 99.35%. In actual use, it can reduce the labor cost of heat exchanger monitoring, so as to stop and clean some units of plate heat exchangers in advance, extend the life cycle and improve heat exchange efficiency.