With the rapid development of 3D digital shape information,content-based 3D model retrieval and classification has become an important research area.This paper presents a novel 3D model retrieval and classification algorithm.For feature representation,a method combining a distance histogram and moment invariants is proposed to improve the retrieval performance.The major advantage of using a distance histogram is its invariance to the transforms of scaling,translation and rotation.Based on the premise that two similar objects should have high mutual information,the querying of 3D data should convey a great deal of information on the shape of the two objects,and so we propose a mutual information distance measurement to perform the similarity comparison of 3D objects.The proposed algorithm is tested with a 3D model retrieval and classification prototype,and the experimental evaluation demonstrates satisfactory retrieval results and classification accuracy.