Abstract: Zero-shot 3D model classification is very important for the understanding and analysis of 3D models.Aiming at the problems of lack of corresponding datasets and low accuracy of zero-shot 3D model classification,a 3D model dataset ZS3D is constructed and a deep learning network ZS3D-Net is proposed.The dataset consists of 41 classes,1677 non-rigid 3D models with complete attributes of all classes,which can be regarded as the benchmark for zero-shot 3D model classification task.For the network,firstly,the visual features of the 3D models are effectively extracted through an ensemble learning sub-network.Then,the correlation between the visual features and semantic features of the unseen and seen classes can be con-structed by a semantic manifold embedding sub-network.Finally,the unseen classes can be recognized based on above two sub-networks.On a traditional 3D model dataset and the proposed ZS3D,ZS3D-Net achieves 30.0%and 58.6%classification accuracy respectively,which are on par or better than the state-of-the-art methods.The experiments also demonstrate that the proposed method has good feasibility and validity.