The identification of plant species is an essential part of botanical study and agricultural production. However, low dimension features cannot describe the leaf information. Thus, it cannot differentiate varieties of plants, and the accuracy is low. A method of plant species identification was proposed based on multi-feature dimension reduction. Firstly, color images of plant leaves were preprocessed by the digital image processing technology. The binary image, gray scale image and texture image without the petiole, wormhole and background were obtained after the preprocessing. Secondly, geometric characteristics and structural characteristic were extracted from the binary image. Hu moment invariants features, gray level co-occurrence matrix features, LBP features and Gabor features were extracted from the gray scale image. The fractal dimension was extracted from the texture images and 2183 features were extracted to describe leaf samples in number. Thirdly, the method of combining principal component analysis (PCA) and linear discriminant analysis (LDA) was adopted to reduce the feature dimension. Then the feature data of training samples was adopted to train the support vector machine classifier. Finally, the support vector machine classifier was used to classify the feature data of test samples. The experiments were carried out on Flavia database and ICL database. The average accuracy was 92.52% and 89.97%, respectively. The experiments showed that the average accuracy of the proposed method was better than that of the compared researches.