Abstract: The secondary flow region of a casing once-through steam generator involves complex two-phase flow. Although numerical simulation methods can achieve precise simulation calculations, they are slow, time-consuming for multi-condition and transient calculations, and consume significant computational resources. Model reduction is a method that transforms a complex system into an approximately simplified system, enabling rapid calculations while retaining the main characteristics of the original system. This study employs the Proper Orthogonal Decomposition (POD) method to reduce the model of the temperature field inside the heat exchange tubes, capturing the modal coefficients by projecting the original complex system onto a limited number of modes. A neural network method is applied to capture the distribution patterns of short- and long-term time series modal coefficients. The results indicate that the prediction reconstruction temperature field error is within 15%, and the prediction speed is improved by four orders of magnitude compared to numerical simulation methods. Therefore, the model reduction coupled with the neural network prediction method established in this study can be used for rapid prediction of the temperature field inside the casing, providing support for internal thermal-hydraulic analysis.