Using spectral emissivity measurement equipment to detect the emissivity of infrared stealth coatings is an important means of monitoring the status of aircraft infrared stealth coatings. During the calibration cycle of the measuring equipment, due to factors such as usage environment, usage frequency, usage method and so on, the condition of the equipment occasionally becomes worse, and the measured values deviate from the reference value which poses a certain risk for timely detection of infrared stealth coating defects and may affect the overall infrared stealth characteristics of the aircraft. To address the issue of measurement deviation during the calibration cycle, a Gramian Angular Field (GAF) and Parallel Convolutional Neural Network (PCNN) calibration status prediction model is established. By collecting one-dimensional time-series data from the device and feeding it into the GAF-PCNN mode, a prediction model for the calibration status of infrared emissivity measurement equipment is trained through deep learning. The experiment shows that the average recognition accuracy of the calibration state prediction model reaches 95%, and the convergence speed is fast and stable, which can be applied to equipment calibration state prediction, prompting early calibration or use beyond the calibration cycle. While ensuring good equipment condition, it reduces equipment calibration activities and improves guarantee efficiency.