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Generative adversarial network (GANs) has significant progress in 3D model generation and reconstruction recently years. GANs can generate 3D models by sampling from uniform noise distribution. But they generate randomly and are often not easy to control. To address this problem, we add the class information to both generator and discriminator and construct a new network named 3D conditional GAN. Moreover, to better guide generator to reconstruct 3D model from a single image in high quality, we propose a new 3D model reconstruction network by integrating a classifier into the traditional system. Experimental results on ModelNet10 dataset show that our method can effectively generate realistic 3D models corresponding to the given class labels. And the qualities of 3D model reconstruction have been improved considerably by using proposed method in IKEA dataset.