Abstract: In infrared-guided semi-physical simulation systems, the temperature field inside the aircraft cabin is often non-uniform, which can have a significant impact on the accuracy and performance of infrared guidance systems. To ensure optimal temperature uniformity within the infrared beam, it is crucial to predict temperatures along the optical path, particularly in regions where temperature sensors cannot be placed. This study focuses on developing a three-dimensional cabin model, and utilizes ANSYS Fluent to simulate and calculate the temperature distributions at various monitoring points under 80 different inlet velocity and temperature conditions. These conditions cover a wide range of operational scenarios, thus providing a diverse and comprehensive dataset for further analysis. The temperature data obtained from these 80 inlet conditions are then used as a training dataset to build a neural network prediction model in Matlab. The model aims to predict unknown temperatures along the infrared beam based on the known temperatures at the monitoring points, which serve as the model's input. In addition to developing the model, the study investigates the relationship between the number and spatial arrangement of prediction points and the overall accuracy of temperature predictions. The results show that the neural network model achieves an overall mean square error （MSE） of less than 0.8%, with the error between the predicted and actual temperatures not exceeding 1.1%. This indicates that the model performs with a high degree of accuracy. The research highlights that both the number of prediction points and their spatial arrangement significantly influence the model's accuracy. The arrangement of prediction points is crucial for ensuring reliable temperature predictions, especially in regions where direct measurements are not feasible. This study provides an effective and practical method for monitoring temperatures within the infrared beam. It also offers valuable insights into optimizing the placement of measurement points to enhance prediction accuracy, ultimately contributing to the development of more reliable and precise infrared-guided systems in complex aerospace environments.