核糖核酸（ribonucleic acid, RNA）序列特征提取广泛应用于RNA分析相关的各个研究领域，如基因表达分析、RNA结构预测或相互作用研究等。考虑到RNA序列由4个核苷酸组成的特点，首次将四元数引入RNA序列的特征提取中。提出了4种新的特征提取方法，分别是纯四元数编码方法、单向叠加方法、中心双向叠加方法和傅里叶变换法，其中两种叠加方法综合了序列整体与局部的频率和位置信息。新提出的4种方法将RNA序列转换为同维数的四元数向量进行特征提取。使用K-最近邻（K-nearest neighbor, KNN）分类器和支持向量机（support vector machine, SVM）分类器与核苷酸组成（nucleic acid composition, NAC）、累积核苷酸频率（accumulated nucleotide frequency, ANF）和三核苷酸组成（tri-nucleotide composition, TNC）等常用特征提取方法比较。结果表明，所提出的几种方法均有较好的表现。

RNA(ribonucleic acid) sequence feature extraction is widely used in various research fields related to RNA analysis, such as gene expression analysis, RNA structure prediction or interaction study. Considering that an RNA sequence is composed of four nucleotides, quaternions were introduced into the feature extraction of RNA sequences for the first time. Four new feature extraction methods were proposed, which were the pure quaternion coding method, the unidirectional superposition method, the center bidirectional superposition method, and the Fourier transform method. Two of these methods combined both the whole and local frequency and position information of the sequence. Four new methods were proposed to convert RNA sequences into quaternion vectors of the same dimension for feature extraction. KNN (K-nearest neighbor) classifier and the SVM (support vector machine) classifier were compared with common feature extraction methods such as NAC (nucleic acid composition), ANF (accumulated nucleotide frequency) and TNC (tri-nucleotide composition). The results show that the proposed methods have good performance.