Abstract: Aiming at solving the problem of species identification of several additives in trace quantity in lubricating oils, a hybrid optimization method for feature band selection of middle infrared spectrum based on binary particle swarm optimization (BPSO) and genetic algorithm (GA) was proposed as GA-BPSO. Firstly, the basic classification model of oil additive species recognition by K nearest neighbor algorithm (KNN) and random forest algorithm (RF) was established. Then the GA-BPSO hybrid optimization algorithm was used to screen the characteristic band region in the whole band range of the spectrum. Then the model recognition accuracy was used as the evaluation criterion and the optimized feature bands were used to build the high-performance enhanced classification model on the basic classification model. In this study, three kinds of lubricating oil additives (isobutylene sulfide (T321), alkyl diphenylamine (T534) and sulphur amine salt phosphate (T307)) with different mixing ratios in the base oil were chosen as samples. The infrared spectrum data of the prepared samples were acquired and divided into training set and test set, and finally imported based classification model and enhance the classification model for training and testing. The results showed that GA-BPSO optimized the selection of feature bands, reduced the effective band length of KNN to the original 16.4%, and improved the recognition accuracy from 70% to 89.58%. The effective band length of the RF was reduced to 15.8%, and the recognition accuracy was improved from 85% to 97.5%. Comparative study showed that the GA-BPSO hybrid feature band optimization method was significantly better than the screening results by either GA or BPSO. This greatly reduced the operating burden and effectively improved the accuracy and stability of multi-type synchronous recognition of the model.