Abstract: Aiming at the problem of tree species identification based on leaf characteristics, this paper self-defines two leaf outline characteristics of leaf angle and edge angle mean on the basis of 25 kinds of characteristics such as leaf texture, invariant moments and traditional shapes, regards the definition and inference of similar polygon as theoretical basis and proposes a kind of classification method for tree species identification which is to construct distance matrix and corner matrix based on leaf outline. Firstly, the tree leaf image was pre-processed to extract the normalized leaf feature vector. Then KNN was used to choose the top 20 of the highest similarity result set. And then distance matrix and corner matrix were constructed to identify and match more accurately. During pre-processing, in order to obtain more accurate characteristics of leaf outline, this paper designs an image preprocessing algorithm of eliminating leaf shadow by taking advantage of significant differences between saturation and chromaticity in HSV color space. During identification and matching, this paper uses the Douglas Peucker approximation algorithm to extract the approximate polygon of leaf outline, and defines calculation methods of distance matrix, corner matrix, matrix element similarity, matrix similarity and comprehensive similarity, and designs a kind of algorithm combining global matching and local matching. The algorithm had been implemented and run on Android mobile phone platform. Results showed that the accuracy of the algorithm was 99.61% among 1 907 complete samples of 32 categories and 94.92% among 851 incomplete samples of 32 categories in Flavia dataset. In Leafsnap dataset, the accuracy of the algorithm was 98.26% of top 5 among 23 147 Lab samples of 185 categories. Compared with other algorithms, this kind of algorithm had higher identification accuracy, better description ability for leaf outline, better robust performance for incomplete leaf, twisted leaf and shadowed leaf, and better practicability and adaptability.