In the conventional near infrared qualitative identification, the maize seed kernel epidermis was not treated with seed coating agent. However, in the actual agricultural production, in order to resist the invasion of diseases and insect pests, improve the germination rate, and achieve the effect of maintaining and increasing yield, maize seeds often need to be coated with seed coating agents. In reality, on the market, it is usually necessary to model maize seed kernels without seed coating to identify the ones with seed coating, so as to achieve the purpose of cracking down fake and shoddy products. The maize seeds coating usually consist of a mixture of insecticides, fungicides, fertilizer, plant growth regulators and other ingredients. Their types are diverse and the components are different. These components contain hydrogen group organic compounds, which have certain absorption to near infrared spectrum. Therefore, the seed coating agent had an interference effect on near infrared spectroscopy qualitative identification, which reduced the performance of some conventional shallow learning model. According to the effects of seed coating on maize variety authenticity identification accuracy, a method of near infrared spectroscopy qualitative modeling based on stacked autoencoder (SAE) neural networks has been proposed. Firstly, taking maize seed spectrum without seed coating agent as the training set, a qualitative analysis model was constructed through SAE unsupervised learning algorithm and Softmax classifier. Then, based on this model, the authenticity of maize seeds with seed coating agents was identified. The experimental results showed that, by using the method based on SAE, the effect of seed coating on maize varietal authenticity recognition rate was controlled within 3%.