To address the issue of insufficient temperature measurement accuracy caused by multiple environmental factors when using infrared thermal imager to monitor abnormal temperature rises in composite insulators, this study proposes a dynamic compensation method that combines a radio frequency identification (RFID) temperature measurement system and infrared thermal imager. This method utilizes the precise temperatures obtained in real-time through the RFID system as calibration values. Environmental parameters such as temperature, humidity, and distance are integrated, and a backpropagation neural network is incorporated to dynamically compensate for infrared measurement results, thereby achieving precise monitoring of the global temperature distribution of composite insulators. Experimental results demonstrate that the model achieves high accuracy, with a coefficient of determination of 0.99964, mean absolute error of 0.10951 ℃, and root-mean-square error of 0.15866 ℃. After temperature compensation using the proposed model, the mean relative error of infrared temperature measurements decreases from 9.62% to 0.41%, and temperature measurement accuracy improves from ±2.26 ℃ to ±0.10 ℃, indicating excellent compensation performance. This study effectively addresses the accuracy limitations of infrared temperature measurement technology, providing more reliable technical support for the condition monitoring and fault diagnosis of composite insulators.