To address the problem of gas infrared spectral identification, a new lifting algorithm named eXtreme gradient boosting (XGBoost) is introduced. Infrared spectral data of chloroform, p-xylene, and tetrachloroethylene are selected for experiments. After these original data are preprocessed, the spectral features are first extracted by feature engineering to generate feature vectors. Then, the XGBoost model is established and its parameters are optimized. Finally, based on a classification accuracy index, the XGBoost model is compared with random forest (RF), support vector machine (SVM), feedforward neural network (FNN), and convolutional neural network (CNN). The experimental results show that XGBoost has a broad application prospect in the field of gas infrared spectral identification.