Building tertiary structures of non-coding RNA is required to understand their function and design new molecules.Nowadays, the development of RNA tertiary structure building methods has provided some possible approaches to obtain lots of RNA 3D models.However, current algorithms of RNA tertiary structure prediction give satisfactory accuracy only for small size and simple topology and many of them need manual manipulation.Here, we present an automatic and fast program, 3dRNA, for RNA tertiary structure prediction with reasonable accuracy for RNAs of larger size and complex topology.We focus on addressing two problems: 1) automatic building of three-dimensional RNA structures and 2) ranking the predictions of the RNA tertiary structures or functional structural motifs.In a benchmark test of about 300 RNA sequences with known experiment tertiary structures with length up to 101 nt, our method not only predicts hairpin and duplex structures with high prediction accuracy comparable to the best algorithm available at present, but also can give satisfactory prediction accuracy for RNA molecules with complex topology and large size, e.g., structures with junctions and pseudoknots.Furthermore, our knowledge-based potential method program is able to rank the best models with the success rate of 98% and capture the structural features (e.g.non-canonical base pairs) with all-atom representation.In both two cases, our method performs consistently better than the existing methods.(AVAILABILITY: http://122.205.6.12 7/3dRNA/3dRNA.html)