The infrared radiometer is an important device for the calibration of the infrared target simulator and is used as a measurement transfer standard during inspections. The primary calibration parameter for the IR target simulator's radiant energy is irradiance, so the role of the IR radiometer is to measure and calibrate its outgoing irradiance. In infrared transmission, the process will be affected by the atmosphere, including two aspects: One is the infrared radiation by atmospheric molecules, aerosol particles scattered or absorbed by the attenuation, the general use of atmospheric transmittance to characterize the degree of atmospheric attenuation of infrared radiation; second is the atmosphere itself emitted by the atmospheric range of radiation will be superimposed on the target radiation. Atmospheric corrections must be made to improve calibration and measurement accuracy.An atmospheric transmission calibration method for infrared target simulators is proposed based on a wide dynamic infrared radiometry method based on a constant standard source. In the calibration of the IR target simulator with a horizontal homogeneous atmospheric approach, a network model of atmospheric transmittance and atmospheric range radiation at different wavelengths, temperatures, and distances is developed using the data analysis capability of convolutional neural networks (Fig.2). Based on the encoder-decoder structure, the detector output voltage under three wavelengths is used as the input of the convolutional neural network, and the test data are normalized and input to the encoder in batches for learning and training, with the batch size set to 8 and the test distance input directly in the embedding layer, and the network is trained according to the training process (Fig.3) to obtain the test distance and atmospheric transmittance and atmospheric range, the model of radiation is given as the correspondence between them.A wide dynamic infrared radiometry method based on a constant standard source was used for multiple experiments to obtain multiple detector output voltage values, which were trained using a network model (Fig.2) to obtain network output values of atmospheric transmittance and atmospheric range radiation at different distances (Fig.7). To verify the improvement of IR radiation measurement accuracy, radiation inversion is performed, and the results of radiation inversion under different methods (Fig.9) can be obtained, and the corresponding IR radiation measurement error graph (Fig.10) and specific values (Tab.2) are shown. The experimental results show that the convolutional neural network algorithm based on the encoder-decoder structure can better predict atmospheric transmittance and atmospheric path radiation, the infrared radiometric average error in three bands of the proposed method is 3.0783%, 3.8186%, 5.3452%, which is far lower than the traditional method, reduces the influence of atmospheric transmittance and atmospheric path radiation, reduces the measurement error of infrared radiation, and improves the calibration accuracy.The atmospheric transmission correction algorithm is proposed for the problem of atmospheric transmission influence using the direct measurement method. Based on the wide dynamic infrared radiation measurement method with a constant standard source, a convolutional neural network algorithm based on an encoder-decoder structure is used to obtain the relationship between atmospheric transmittance, atmospheric range radiation and waveband and test distance, and atmospheric correction is performed for different wavebands and different test distances. Compared with the traditional method, there is no need to use MODTRAN software to calculate atmospheric transmittance, atmospheric range radiation and atmospheric parameters of the measurement experiment environment, which improves the problem of low distance resolution and accuracy of MODTRAN software under close measurement conditions and improves the accuracy of infrared radiation measurement.