With the commissioning year of LAMOST, the data archive of one-dimensional spectra is being released gradually. Searching for special objects like cataclysmic variables (CVs) in the data is one of the objectives of LAMOST. This paper presents a novel method to identify CVs from the optical spectra by using a support vector machine (SVM) technique combined with principal component analysis (PCA). After dimension reduction and feature extraction by PCA, spectral data are classified by the SVM and most of the non-CVs are excluded. The final reduced list can be identified manually or by a template matching algorithm. Experiments show that this data mining method can find CVs from the LAMOST database in an effective and efficient manner. We report the identification of 10 cataclysmic variables, of which 2 are new discoveries (Fig. 1). In addition, this method is also applicable to mining other special celestial objects in sky survey telescope data. The paper titled“Data mining for cataclysmic variables in the Large Sky Area Multi-Object Fiber Spectroscopic Telescope archive” has been published in Monthly Notices of the Royal Astronomical Society.

                                 Fig. 1 Newly identified CVs