As a new detection method, near infrared spectroscopy (NIR) has been applied to the rapid compositional analysis of cement raw meal. However, environmental factors such as humidity may have a potential impact on its detection. In order to improve the accuracy of the detection, we studied compensation method based on the impact of humidity upon near infrared spectroscopy of cement raw meal. Twenty four cement raw meal samples were obtained from cement factories. Eighteen of them were used as the calibration set; six of them were used as the validation set. The effective ingredients in cement raw meal were SiO2, Al2O3, Fe2O3, and CaCO3. The standard value of the contents of each ingredient was measured by X-ray fluorescence analysis. Firstly, eighteen samples of calibration set were repeatly measured five times, and ninety spectra were obtained, which were used to establish model Ⅰ. Then five humidity gradients were made for eighteen samples of calibration set. The process to generate the humidity gradient sample was as follows: first, the samples were placed on an electric heating platform, flattened with glass rod and heated at 180 ℃ for 30 minutes, then the samples were placed on the radiation fin to cool. When the sample restored to room temperature, they were taken out and a spectrum was obtained. The samples were placed in the agitator, sprayed with deionized water for two times, and then stirred for 30 seconds to be mixed evenly. After that, the mixed sample was measured to get the next spectrum. Five spectra with certain humidity gradients were obtained by repeating the process. The humidity of all samples was measured by drying method. The range of humidity change was within 0.6%～2%. Each sample with certain humidity was measured once, and these ninety spectra were used to establish the model Ⅱ. Then, five humidity gradients were made for the validation set in the same way as the calibration set. Thirty spectra were obtained by detecting each humidity gradient sample in the validation set. All spectra were pre-processed by multivariate scattering correction, and the fitting band was 4 000～5 000 cm-1. Partial least squares method was used for modeling. Comparing the five humidity gradients of the same sample, we could see that the spectra have the greatest differences at 5 200 cm-1, and there were also obvious differences at other locations, so the humidity change has a significant impact on the whole spectrum. Finally, the root mean square error of prediction (RMSEP) of the 30 spectra in model Ⅰ and model Ⅱ were compared. The RMSEP of SiO2, AlO3, Fe2O3 and CaCO3 in model Ⅱ was reduced by 25%, 31.3%, 33.3% and 25% compared with model Ⅰ. The experimental results show that the humidity of cement raw meal sample has a certain influence on the prediction results of near infrared spectroscopy model. Modeling with humidity gradient samples can effectively reduce the influence of humidity on the prediction results.