Traditional Locally Linear Embedding (LLE) manifold learning algorithm uses Euclidean distance to measure neighbor points. However, Euclidean distance represents the straight line distance between two points and does not necessarily reflect the actual data distribution in a high dimension space, which leads to the instability of neighbor point selecttion. In order to solve this problem, an algorithm based on Correlation Neighbor LLE (CN-LLE) and Correlation Nearest Neighbor (CNN) classification is proposed. This algorithm uses the correlation coefficient of data to measure the neighbor points and to achieve more effective local reconstruction to extract the distinguishing character. Then, it uses the CNN to classify the reduced dimension data. The experiment results from KSC and Indian Pine hyperspectral remote sensing data sets show that the total accuracy of the proposed CN-LLE+CNN algorithm is improved by 2.11%-11.55% and the Kappa coefficient is improved 0.026-0.143 as compared with those of LLE, LLE+CNN and CN-LLE. The CN-LLE+CNN algorithm increases the probability of the same class neighbor, can extract the distinguishing characters of the same data effectively and has a better stability. This algorithm can effectively classify hyperspectral remote sensing data of ground objects.