The segmentation of foliage organs from 3D point clouds is an elemental work of forestry informatization measurement. However, the foliage point cloud data has a similar color, and the point construction is complex which ca n not be expressed easily. Therefore, a novel feature called local tangent p lane distribution is proposed, and fused with original data, scatter spatial distribution and n ormal distribution to construct a multi-dimension feature, which can character i ze different foliage organs more effectively. Then three kinds of classi fiers, including standard SVM, PSVM, GEPSVM, are used as a compa rison. And then the graph cut is also utilized for a re classification at subsequent process i ng to improve the classification performance. A variety of comparat ive experimental results show that the proposed mutli dimension feature seg mentation method can effectively classify the foliage organs from point cloud d ata. The recognition rate can reach 98%.