In order to achieve quick and nondestructive prediction of cucumber disease, a prediction model of greenhouse cucumber downy mildew has been established and it is based on analysis technology of laser-induced chlorophyll fluorescence spectrum. By assaying the spectrum curve of healthy leaves, leaves inoculated with bacteria for three days and six days and after feature information extraction of those three groups of spectrum data using first-order derivative spectrum preprocessing with principal components and data reduction, principal components score scatter diagram has been built, and according to accumulation contribution rate, ten principal components have been selected to replace derivative spectrum curve, and then classification and prediction has been done by support vector machine. According to the training of 105 samples from the three groups, classification and prediction of 44 samples and comparing the classification capacities of four kernel function support vector machines, the consequence is that RBF has high quality in classification and identification and the accuracy rate in classification and prediction of cucumber downy mildew reaches 97.73%.