Apple watercore occurs in many major apple producing areas, while there is no suitable way to sort apple type with watercore quickly. This research applies near infrared transmission spectroscopy, chemometric methods and manifold learning algorithm, selecting Xinjiang Red Fuji apple and watercore disease ones as samples, collecting near infrared transmission spectrum within 590 to 1 250 nm, spectroscopically corrected spectrum is used to do ten more speciesof spectral pretreatment. Firstly, full-wavelength pattern recognition is performed on the pre-processed spectral data to find out that multivariate scattering correction is the best pretreatment method. Then dataset preprocessed by multivariate scattering correction is used to make dimension reduction by using many other manifold learning algorithms such as Multidimensional Scaling, Stochastic Neighbor Embedding, Symmetric Stochastic Neighbor Embedding, t-Distributed Stochastic Neighbor Embedding, Laplacian Eigenmaps, Isomap, Landmark Isomap, Locally Linear Embedding, Diffusion Maps, combining Mahalanobis distance discrimination, quadratic discriminant analysis, K-nearest neighbor method to identify if watercore exist or not. Results indicate that an optimal identification model is obtained by using MSC-Landmark Isomap-KNN when principal components equal to twelve, and the identification rates for the calibration set and prediction set are 97.5% and 96.3% respectively. Hence, manifold learning algorithm and near infrared transmission spectroscopy technology can successfully realize the watercore identification of Xinjiang Red Fuji apple, which provides a theoretical basis for developing identification device in further research.