针对矿物油三维荧光谱特征提取的奇异值分解方法(SVD)容易忽略重要小成分特异信息的不足，提出小波变换(WT)和SVD 相结合的特征提取方法。利用WT 获取矿物油三维荧光谱数据的低频主部近似分量和不同方向的细节分量；用SVD 提取综合矩阵的奇异值特征；使用模糊C 均值聚类(FCM)方法对矿物油三维荧光谱样本数据进行分类识别，并引入随机噪声进行进一步测试。结果表明WT-SVD 特征向量在矿物油分类识别方面比单独SVD 特征向量具有准确度高、稳健性强的优势，有助于更好地实现矿物油聚类分析或种类鉴别。

Singular value decomposition (SVD) has a shortcoming in feature extraction of mineral oil′ s 3D fluorescence spectrum, as it easily discards small eigen values that may be important for identification. A new method that combines wavelet transform (WT) and SVD in feature extraction is presented. Wavelet approximation components of mineral oil′s 3D fluorescence data and detail components in different directions are obtained, and their singular value feature is extracted. The fuzzy clustering method (FCM) is used to classify or discriminate mineral oils, and a further test is carried out with random noise introduced. The result shows that WT-SVD feature vector is superior to SVD in mineral oil classification or oil identification, with higher accuracy and robustness than SVD in anti-jamming performance.