Different cultivars of mangoes can not only represent different qualities but also produce different economic benefits. Traditional mango variety identification methods often rely more on the experience of practitioners and are time-consuming and laborious. Therefore, how to quickly classify mango cultivars is an emerging problem that needs to be solved. Near-infrared (NIR) spectroscopy technology is a fast and non-destructive approach. Users can often identify different mangoes by combining machine learning methods with near-infrared spectroscopy data. However, the NIR spectral information of the same variety of mangoes can vary due to variations in different instruments, seasons, and years. These differences result in a different distribution between the previously measured sample data (source domain) and the newly measured sample data (target domain). Consequently, the present classification model cannot correctly classify new mango samples. Domain adaptation methods can solve this problem of model inapplicability caused by different data distributions. This article focuses on the distribution differences of mango near-infrared spectroscopy data caused by factors such as working temperature and season. The domain adaptation methods can solve the problem of model unsuitability caused by different data distributions. This article used a deep domain adaptive neural network (DANN) model to solve this problem. The DANN model effectively achieves cross-domain sample classification models by aligning features between two domains through adversarial learning. This article compared DANN with unsupervised dynamic orthogonal projection (uDOP) and joint distribution adaptation (JDA), two traditional domain adaptation methods based on statistical learning. The experimental results of applying these three methods in this article showed that the DANN model could achieve a classification accuracy of 94% for the test set in the binary classification task of mango varieties. In the multi-classification task of mango varieties, the classification accuracy of the DANN model was over 10% higher than that of uDOP and JDA. The results indicated that the DANN model could effectively solve the problem of mango variety recognition caused by the different distribution of near-infrared spectral data between two fields.