The product nondestructive classification testing method based on deep convolutional neural network was studied to realize automatic production, optimize product classification, improve production efficiency and product quality control level. The principle of laser absorption spectrum technology is analyzed, and a near infrared laser absorption spectrum acquisition device is designed to collect the near infrared laser absorption spectrum of the product to be tested. Savitzky-Golay method was used to pretreat the absorption spectra, reduce the interference between spectra, and enhance the purity and sensitivity of spectra. A deep convolutional neural network model with four hidden layers is constructed, and cross-entropy is used as a cost function to implement backpropagation training on the network model. The near-infrared laser absorption spectrum of the pre-processed product to be tested is input into the trained deep convolutional neural network model, and the output result is the nondestructive classification test result of the product to be tested. Experiments show that this method can effectively realize the nondestructive classification testing of products, and the classification recognition rate of different types of products can reach more than 97%, the maximum detection time is 1.11 s, and the detection efficiency is higher.