An identification method based on sparse representation (SR) combined with autoencoder network (AN) manifold learning was proposed for discriminating the varieties of transmission fluid by using near infrared (NIR) spectroscopy technology. NIR transmittance spectra from 600 to 1 800 nm were collected from 300 transmission fluid samples of five varieties (each variety consists of 60 samples). For each variety, 30 samples were randomly selected as training set (totally 150 samples), and the rest 30 ones as testing set (totally 150 samples). Autoencoder network manifold learning was applied to obtain the characteristic information in the 600~1 800 nm spectra and the number of characteristics was reduced to 10. Principal component analysis (PCA) was applied to extract several relevant variables to represent the useful information of spectral variables. All of the training samples made up a data dictionary of the sparse representation (SR). Then the transmission fluid variety identification problem was reduced to the problem as how to represent the testing samples from the data dictionary (training samples data). The identification result thus could be achieved by solving the L-1 norm-based optimization problem. We compared the effectiveness of the proposed method with that of linear discriminant analysis (LDA), least squares support vector machine (LS-SVM) and sparse representation (SR) using the relevant variables selected by principal component analysis (PCA) and AN. Experimental results demonstrated that the overall identification accuracy of the proposed method for the five transmission fluid varieties was 97.33% by AN-SR, which was significantly higher than that of LDA or LS-SVM. Therefore, the proposed method can provide a new effective method for identification of transmission fluid variety.