A selection method of infrared spectral based on cuckoo search was proposed to meet the classification of grease. The method would help to remove the infrared spectral region affected by noise and environment effectively, and realized the feature selection and dimension reduction processing of large spectral data. A more accurate and efficient classification model of grease was established by selecting the optimal spectral bands. Regarding the infrared spectrum data of three different types of greases as research targets in this paper, the Principal Component Analysis (PCA) was applied to compress the Infrared spectrum data of different bands and extracted the main components. Using the extracted main components of IR spectra and the grease thickener category as input and output respectively, an accuracy optimization training for the weight of principal component and parameter of classification kernel was conducted by Cuckoo Search (CS) to establish the classification prediction model. The classification accuracy of the model was tested and obtained the accuracy of the test results of model. In addition, it established the link between the infrared spectral band and the accuracy to get the optimal class identification model and optimal classification bands. The classification accuracy of the model was tested, and the result showed that the main feature trained and weighted by Cuckoo Search presents obvious clustering phenomenon. The classification kernel could be found and the type of grease could be classified accurately. Furthermore, it provided recommended bands and characteristic peaks for distinguishing different greases in the process of searching. The correct identification probability of the grease was improved from 94.44% for the classification model by whole band to 100% for filtered feature band, reducing the operation time and improving the search efficiency.