Locally Linear Embedding (LLE) manifold learning algorithm needs to calculate the neighbor points of each image based on Euclidean distance. But this method represents only the straight line distance between two points and does not necessarily reflect the actual distribution relationship of the image data sets in the high dimensional space. In order tosolve this problem, an approach based on the fusion data between angle and Euclidean distance of images is proposed to calculate the neighbor points of LLE and to classify data. This method uses the fusion data between angle and Euclidean distance of images to measure the adjacent relations of image data points and find k neighbor points, which can achieve more effective local reconstruction to extract the distinguishing features. Finally, the nearest neighbor classifier with angle of images is used to classify the image data. Experiments on KSC and Indian Pine database show that the overall accuracy of this proposed algorithm is improved by 1.54%~6.91% compared with LLE algorithm.