For airborne electronic equipment, temperature is the most important factor affecting its performance, so it is very important to invest its dynamic temperature response process in a flight environment. By analyzing the heat exchange relationship between different devices in an electronic wing pod cabin, a temperature prediction method for electronic pod cabin based on the Random Vector Functional Link (RVFL) neural network is proposed in this paper. This method can complete a construction of prediction model using small amount of data. Hence it can realize a quick temperature response prediction only with the initial temperature values, and ensure relative high prediction accuracy.