Anthracnose of Camellia oleifera is a highly destructive disease commonly occurring in the Camellia oleifera industry, seriously restricting the development of the Camellia oleifera industry. In theearly stage of Camellia oleifera anthrax. It only need to repair the sick part of the tree in time. As the disease worsens, the affected branches must be eradicated, and seriously sick strains should be cut down in time. Aiming at, the current problem that the detection of Camellia oleifera anthrax is complex and the judgment accuracy is low, this paper proposes a method to determine the detection of the degree of Camellia oleifera anthracnose using laser-induced breakdown spectroscopy (LIBS) and Fourier transform near infrared spectroscopy (NIR), to achieve rapid, efficient and high-precision determination of the degree of anthracnose of Camellia oleifera. Fe, Ca, Mn, CaⅡ, and other elements in the LIBS spectrum of healthy Camellia oleifera leaves and diseased Camellia oleifera leaves were significantly different， and the characteristic peak intensities of the elements increased with the degree of disease. The main reason is that these elements are all necessary elements for the growth of Camellia oleifera. The absorbance of the Fourier transform near-infrared spectra of healthy Camellia oleifera leaves and Camellia oleifera leaves with different degrees of anthracnose also differs, mainly due to the ability of Fourier transform NIR to extract the physical properties of the sample. Using normalization, multivariate scatter correction (MSC), standard normal variate (SNV) preprocessing method combined with competitive adaptive reweighted sampling (CARS), Successive projection algorithm (SPA) variable screening method to establish fusion spectral classification model of anthracnose grades of Camellia oleifera by partial least squares discrimination analysis (PLS-DA) and support vector machine (SVM). Among them, the Root mean square error of prediction (RMSEP) and the prediction determination coefficient R2p of LIBS (Normalization-CARS)-NIR (Normalization-CARS)-PLS-DA of prediction set are 0.173 and 0.987 respectively and the misjudgment rate is 0. In the SVM model, the accuracy of the modeling set of LIBS-NIR-CARS-SVM is 100%, and the accuracy of the prediction set is 97.59%. The experimental results show that: the PLS-DA model based on the fusion of LIBS and Fourier transform NIR spectra for detecting anthracnose grades of Camellia oleifera leaves higher qualitative analysis accuracy and more stability than the SVM model. The results showed that: the LIBS spectrum combined with Fourier transform NIR spectrum could be used to separate healthy Camellia oleifera leaves from various grades of anthracnose of Camellia oleifera leaves efficiently, quickly and accurately.