Powdery mildew is one of the common diseases of cucumber, which has a rapid propagation speed and can cause a large reduction of cucumber. Quick and accurate recognition of cucumber powdery mildew has great significance for the diagnosis and control of cucumber diseases. Utilize visible spectrum technology combined with principal component analysis and support vector machine algorithm can realize the quick recognition of cucumber powdery mildew. Sphaerotheca fuliginea was used to make spore suspension and inoculated it into the cucumber leaves in a scientific research solar greenhouse to induce powdery mildew. When the powdery mildew occurred, the spectral information of cucumber leaves was collected by the Ocean Optics USB2000+ portable spectrometer. Five point sampling method was used to collect samples, two cucumber plants were inspected at each point and four leaves were checked on each plant, and five areas were chosen randomly on each leaf to use to spectral information acquisition. Then 200 samples of cucumber powdery mildew leaves were got , and 200 healthy leaf samples were collected as contrast by the same method. the standard white plate and dark current was Utilized to calibrate the spectrometer. The integral time and the scanning times were adjusted and the smoothness parameters of Ocean Optics Spectra-Suite software was used to smooth spectral curves and suppress noise. Through classification and recognition of spectral features, the spectral bands with big noise was removed and the 450~780 nm visible light band was chosen as the research range. The principal component analysis (PCA) was used to reduce the dimension of high-dimensional spectral data (947 dimension). According to the cumulative contribution rate of principal components, the former 5 principal components were chosen as input variables and the discriminant results as the output to build the classification model. We utilized support vector machine (SVM) algorithm and randomly took 120 samples as the training set to build the classification model, and the rest 80 samples as testing set for model checking, and the optimal model was obtained by selecting different kernel functions. The confusion matrix was used to evaluate the accuracy of the classification model, when the radial basis kernel function was selected, the recognition accuracy of the classification model for cucumber healthy leaves and powdery mildew leaves were respectively 100% and 96.25%, and the total accuracy was 98.125%. The results showed that the visible light spectrum analysis combined with PCA and SVM algorithm could be used to identify cucumber powdery mildew quickly and accurately, which provides a method and reference for the diagnosis of cucumber diseases.