The satellite-based infrared hyperspectral Geostationary Interferometric Infrared Sounder (GIIRS) can achieve high vertical resolution observations of atmospheric temperature and humidity parameters, which provide a more accurate initial field for numerical weather forecasting. Based on GIIRS observation radiation, a back propagation (BP) neural network and deep learning convolutional neural networks (CNNs) are used to retrieve atmospheric temperature and humidity profiles, and the focus is on the construction of the CNN model and the optimization of parameters, thus obtaining the network model configuration with the highest retrieval accuracy. The training samples are divided into three schemes according to different surface types and the influence of whether there are clouds (scheme 1: no classification, scheme 2: land or ocean surface, scheme 3: clear or clouds) and modelling, retrieving and testing. The results show that the two retrieval algorithms both have good retrieval precision. Relatively speaking, the CNN method has a smaller retrieval bias, root-mean-square error and mean relative error at all altitudes, and the retrieval precision is higher. The temperature retrieval of the CNN method is greatly improved in the high level at 10-200 hPa, and the maximum values of the three classification schemes are 1.15 K, 1.06 K, and 1.02 K, respectively, and the humidity retrieval of the CNN method also shows improvement in the lower troposphere at 500-1000 hPa, and the averages of the three classification schemes are 0.43 g/kg, 0.41 g/kg, and 0.34 g/kg, respectively. The third scheme (clear or clouds) of the BP neural network method has the best retrieval precision of temperature and water vapour mixing ratio profiles, and the first scheme (no classification of sample data) of the CNN algorithm has the most accurate retrieval results.