With the development of computer graphics and three-dimensional (3D) modeling technology, 3D model retrieval has been widely used in different applications, such as industrial design, virtual reality, medical diagnosis, etc. Massive data brings new opportunities and challenges to the development of the 3D model retrieval technology. However, with the emergence of complex models, traditional retrieval algorithms are not applicable to some extent. One important reason for this is that the traditional content-based retrieval methods do not take the spatial information of 3D models into account during feature extraction. Therefore, how to use the spatial information of a 3D model to obtain a more extensive feature has become a significant issue. In our proposed algorithm, we first normalize and voxelize the model, and then extract features from different views of the voxelized model. Secondly, deep features are extracted by using our proposed feature learning network. Then, a new feature weighting algorithm is applied to our 3D-view-based features, which can emphasize the more important views of the 3D models, so the retrieval performance can be improved. The experimental results on the standard 3D model dataset, Princeton ModelNet10, show that our model can achieve promising performance.