This study proposed a rapid, non-destructive method for identifying Wuchang Daohuaxiang rice based on near-infrared (NIR) spectroscopy combined with machine learning algorithms. NIR spectra of different varieties of rice were collected. First-order derivative was determined as the best spectral preprocessing method using partial least squares regression (PLSR). Two dimensionality reduction methods, principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), were compared, and five machine learning models including artificial neural network (ANN), K-nearest neighbors (KNN), random forest (RF), decision tree (DT), and Naive Bayes (NB) were constructed for variety classification and comparison. The results showed that t-SNE improved the Calinski-Harabasz index by 1078.0051, demonstrating better clustering performance. After t-SNE dimensionality reduction, the performance of all five models was superior to that without dimensionality reduction. The average classification accuracy was 95.78%. The accuracy of the NB model was improved most effectively (by 18.89%). The random forest model showed the best classification performance, with prediction accuracy and precision of 98.89% and 98.96%, respectively. This method provides a rapid, non-destructive solution for identifying Wuchang Daohuaxiang rice, which will contribute to brand protection and safeguarding consumer rights, and also offers a new approach for the identification of other geographical indication agricultural products.