Convolutional neural network (CNN) was widely used in image classification and recognition but its application in near infrared spectroscopy has not been reported. Therefore, the near-infrared spectroscopy classification modeling method based on CNN was studied in this paper. Taking into account the characteristics of near-infrared spectral data, an improved CNN modeling method was presented in this paper, which improves the CNN classical model Lenet-5: ①The square matrix convolution kernel was transformed into a vector convolution kernel for one-dimensional near-infrared spectroscopy. ②The C5, F6 and output layers of the lenet-5 structure were changed to single-layer sensing machines in order to simplify the network structure. At the same time, the method of sampling points was used to reduce the dimensionality of near infrared spectrum and speed up the convergence rate. The influence of convolution kernel size on modeling results was also studied in this paper. NIR-CNN model was established by the near-infrared spectroscopy of 600 central tobacco samples from northeast, Huanghuai and southwest China. The accuracy of the model was 98.2% and 95% for the training set and test set. The experimental results showed that the application of CNN could accurately and reliably identify the near infrared spectrum data. This method provided guidance for the scientific and rational utilization of raw materials of tobacco enterprises, and it was important to maintain the quality stability of cigarette products. The method of near infrared spectroscopy based on CNN could also be applied in the classification of other agricultural products.