Comparing ribonucleic acid (RNA) secondary structures of arbitrary size uncovers structural patterns that can provide a better understanding of RNA functions. However, performing fast and accurate secondary structure comparisons is challenging when we take into account the RNA configuration (i.e. linear or circular), the presence of pseudoknot and G-quadruplex (G4) motifs and the increasing number of secondary structures generated by high-throughput probing techniques. To address this challenge, we propose the super-n-motifs model based on a latent analysis of enhanced motifs comprising not only basic motifs but also adjacency relations. The super-n-motifs model computes a vector representation of secondary structures as linear combinations of these motifs.