Benefited from advances of neuron tracing techniques, the ever-increasing number of digitally reconstructed 3D neuron images have greatly facilitated the research in neuromorphology. However, the sheer volume and the complexity of these 3D neuron data pose significant challenges for computational analytics, e.g., effectively finding neurons sharing similar morphologies, identifying neuron types, correlating neuron morphologies with properties, all of which require accurate measuring and fast indexing methods especially designed for the massive 3D neuronal images. In this paper, we present an accurate and efficient framework for the computational analytics of 3D neuronal structures based on advances of deep learning and data mining techniques. Particularly, unlike previous methods quantitatively describe neurons by measuring pre-defined metrics according to the tree-topological structures, we first develop a new method for the morphological feature representation by a proposed 3D neuron mapping and a modified generative adversarial networks (GANs). Subsequently, considering the computational complexity when retrieving large-scale neuron datasets, we integrate the neuron features with graph-based indexing, which can significantly improve the retrieval efficiency without losing accuracy. Experimental results show that our framework can effectively measure the similarity among massive neurons (e.g., neurons), outperforming state-of-the-arts with more than 10% in accuracy and hundreds of times in efficiency improvements.