In order to improve the limitation of traditional debris flow hazard assessment methods, a SVM-based debris flow hazard assessment method was proposed. Seven factors including the most volume of once flow (L1), frequency (L2), watershed area (S1), valley length (S2), watershed relative height difference (S3), valley incision density(S6) and the length ratio of sediment supplement (S9) were chosen as assessment factors of debris flow hazard degree. Using support vector machine (SVM) theory, selecting Radial Basis Function, and using trial-and-error method for optimal selection of parameters, C=8, r=2. Thirty seven debris flow channels with 259 basic data in Yunnan Province were selected as training samples, and an assessment model based on SVM was created. The model was applied to evaluating debris flow hazard degree of Jishi Valley hydropower station of Huanghe (Yellow) River. Assessment result consistency came to 73.33% comparing to fuzzy mathematic method. The results show that the model has advantage of best generation, high training speed, and convenient for modeling through an instance application. It will be thought as being broad application scope that SVM was applied to hazard assessment of debris flow.