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Recognition of 3D objects is among the most popular topics in computer vision, and to find an effective representation of 3D models is a key issue. This paper proposes a novel way to describe 3D models in 3D object recognition system. We select three 2D shape features based on their complementarities, and implement feature fusion with coefficients obtained by self-learning method to form a concatenate feature with better robustness. Isomap manifold-learning-based clustering is introduced for more effective selection of representative views, because its non-linear property adapt to the 3D view sphere of objects very well, thus resulting in images with better representativeness. To test the effectiveness of this representation, a 3D object recognition system is established. Experiments on Princeton Shape Benchmark show the recognition rate of our method is comparative with state-of-the-art 3D model retrieval methods. The well-performed system can be a proof of the advancement of our method of 3D model representation.