Infrared spectrum analysis plays an important role in many fields such as natural science, engineering technology, and so on. With the continuous development of computer and artificial intelligence technology, higher requirements have been imposed on infrared/near-infrared spectral analysis. Based on artificial neural networks, the deep learning algorithm performs representation learning by extracting hierarchical features from data layer by layer. It has unique advantages in analyzing the details features of data. It has been successfully applied in many fields such as computer vision, speech recognition, and disease diagnosis. Although deep learning has achieved good results in the analysis of images, audio, and text data, its application in infrared/near-infrared spectral analysis is still very limited. A deep learning convolution operation method for infrared spectroscopic analysis is presented. Firstly, one-dimensional Fourier Transform Infrared Spectroscopy (FTIR) data are transformed into two-dimensional RGB color image data through Symmetrized Dot Patterns (SDP), and then, the transformed SDP color image data is fed into the VGG (Oxford Visual Geometry Group) deep convolutional neural network for deep learning to establish a classification and recognition model. By SDP transformation, the infrared spectra of sevensingle-component gases of different concentrations, including methane (CH4), ethane (C2H6), propane (C3H8), n-butane (C4H10), iso-butane (iso-C4H10), n-pentane (C5H12), iso-pentane (iso-C5H12), and its mixtures convert to 224×224 color images. The SDP transformed images show a significant difference in the distribution of the pattern points and are more in line with the data format of the VGG convolution operation. The SDP-VGG method is used to identify the methane concentration range in gas logging: the gas logging gas is a mixture of the above seven components of alkanes, and the concentration ranges of methane are divided into five categories: <20%, 20%～40%, 40%～60%, 60%～80%, and 80%～100%. The infrared spectra of different seven-component alkane mixed gas samples are collected by the infrared spectrometer in the wavenumber range of 4 000～400 cm-1 and scanning interval 12 nm. Without special pre-processing and feature extraction, 4 500 samples are used to establish the identification model of various methane concentration ranges by the SDP-VGG method. The recognition accuracy of the SDP-VGG model reached 91.2%, which is better than the recognition accuracy of 88.7% and 86.2% of the Support Vector Machine (SVM) and Random Forest (RF) models established by the same infrared spectral data. The research shows that SDP combined with deep learning can accurately extract the key features of infrared spectra. It is a more effective infrared spectral analysis method, which improves the recognition accuracy of the infrared spectrum and has broad application prospects.