The laser induced breakdown spectroscopy (LIBS) method is used for the rapid and green identification of Gannan navel orange juices. The sugar contents and Ca,K,Zn element contents of healthy and Huanglongbing navel oranges are experimentally measured. In addition, the differences in sugar and element contents are analyzed. The LIBS data of navel orange juice is first collected, which is then preprocessed by the nine-point smoothing (9SM) method combined with multivariate scattering correction (MSC). Finally, the principal component analysis (PCA) method combined with the multi-layer perceptron (MLP) neural network and radial basis function (RBF) neural network model is used for rapid identification of healthy and Huanglongbing navel orange juice. The results show that the PCA-MLP model is superior to the PCA-RBF model in the identification effect of healthy and Huanglongbing navel oranges. The identification accuracies of healthy and Huanglongbing navel oranges on the training dataset are 93.8% and 93.4%, respectively. In contrast, the identification accuracies of healthy navel oranges and Huanglongbing navel oranges on the prediction dataset are 93.9% and 94.8%, respectively. The LIBS detection results confirm that the Huanglongbing results in the change in pulp quality of navel oranges. The further spectral preprocessing and the classification model are used to distinguish the juices of Huanglongbing oranges and healthy navel oranges in quality and thus the product qualification ratio of factory orange juices is increased.