To find a fast, accurate, and effective method for the identification of honey adulteration, near-infrared spectroscopy combined with chemometrics was used to analyze natural honey and adulterated honey in this paper. First, 224 samples were collected for the study, including 112 natural pure honey samples from 20 common honeys in China, and 112 adulterated honey sample were prepared with 6 different syrup samples according to different syrup contents(10%, 20%, 30%, 40%, 50%, or 60%). Near infrared spectral data (wavelength range of 400～2 500 nm) of all samples were obtained by near infrared light instrument scanning. Then, first derivative (FD), second derivative (SD), multiple scattering correction (MSC), and standard normal variation (SNVT) pre-processing of the original spectra combined with PLS-DA (linear algorithm) and SVM (non-linear algorithm) modeling, respectively, were adopted to establish a differential model of natural honey and syrup-adulterated honey and compare the effects of different pretreatment methods on the honey adulteration identification model established by the two different modeling algorithms. The penalty parameter c and the kernel function parameter g of the SVM algorithm were optimized by three optimization algorithms: grid search, genetic algorithm, and particle swarm optimization. The analysis results showed that the PLS-DA model established by the FD preprocessing had the best effect, and the accuracy of the best PLS-DA model was 87.50%. After MSC pre-processing, the SVM model with the penalty parameter c of 3.031 4 and the kernel function parameter g of 0.329 8 was the best. The accuracy of the best SVM model was 94.64%. It can be seen that the non-linear SVM algorithm combined with the NIR spectral data natural honey and syrup-adulterated honey identification model is better than the PLS-DA model.