The camellia oleifera industry has good economic and ecological benefits, and is highly valued by the state. At present, the anthracnose disease encroaches camellia oleifera tree day by day aggravates, reduces the production seriously, causes the benefit of camellia oleifera industry to suffer directly. So it is necessary to find a fast, accurate and convenient method for anthracnose detection. Laser-Induced Breakdown Spectroscopy (LIBS) is a low-cost, slightly damaged, and no-residue technology that can quickly and real-time detect a variety of ingredients. The qualitative detection method of anthracnose of camellia oleifera was studied by LIBS combined with stoichiometry. The samples were collected from the camellia oleifera planting area. 100 healthy camellia oleifera leaves and 100 anthracnose infected camellia oleifera leaves were collected respectively. The collected blades were micro-treated, namely, the surface stains of the blades were washed repeatedly to remove, then classified, bagged and labeled, and finally LIBS spectrum acquisition experiment was carried out. The experimental equipment was MX2500+ of ocean optics, the LIBS experimental parameters were set as 50 mJ laser energy, and the optimal delay time was 2. Six spectral data were collected from each blade and averaged. The characteristic peak of Si was observed at 251.432 nm of the LIBS spectrum of camellia oleifera leaves, and the characteristic peak of Fe was observed at 252.285, 259.837 and 385.991 nm, and the characteristic peak of Mn was observed at 260.568, 279.482 and 280.108 nm, respectively. Results: the LIBS signals of trace elements Si, Fe, Mn in camellia oleifera leaves are directly related to the health degree of camellia oleifera leaves. In addition, this experiment used LIBS technology combined with MSC spectral pretreatment and PCA classification to classify the two states of camellia oleifera leaf health and anthracnose infection. The contribution rates of PC1, PC2 and PC3 are 80%, 12% and 6% respectively. The establishment of three-dimensional model classification can clearly distinguish the two states of camellia oleifera leaves. At the same time, the PLS-DA model was also used in this paper, and the recognition rate of the model was up to over 90%, which could be used to better classify the two categories of camellia oleifera leaves. The above two stoichiometric methods can distinguish the health and disease of camellia oleifera leaves. The results showed that it was feasible to detect anthrax of camellia oleifera by LIBS. Quantitative detection of trace elements and nutrient elements in camellia oleifera leaves can be carried out by using LIBS technology, which provides a reference for quantitative detection. A new method for rapid detection of anthracnose of camellia oleifera.