Vector Space Model (VSM) was originally used in document retrieval. It is characterized by extracting the texts from the document and converting the document into a text vector space. VSM compares the similarity between document text vectors and text retrieval text vectors. The document retrieval is accomplished according to the similarity based on the nearest template principle of template matching in pattern recognition. This article applied such principle into the identification of samples based on the characteristic of LIBS spectrum. To create the database of characteristic peaks of training dataset, we obtained the spectra template of all kinds of samples from the training dataset and extracted the wavelength and intensity of peaks from the spectra template. In another hand, to create the database of characteristic peak vectors for all the training samples, we calculated the spectral characteristic peak weight and converted the spectra to peak vector space. The characteristic peak vector of the test dataset was obtained by the same way. The cosine value between the characteristic peak vector of test data and every characteristic peak vector in the database were calculated and the maximum cosine was taken as the identification result. Geological cuttings, the research subject, were identified by the VSM in this paper. The result demonstrated that the VSM could rapidly identify the spectra from 4 kinds of geological cuttings’ LIBS spectral and the correct identification rate was 100% after the spectra of the test dataset were averaged. The proposed LIBS spectral identification method based on VSM can be expanded to the identification of other spectral data.