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The research work in this paper follow four steps: define warning situation, seek warning sources, analyze warning omens, foretell warning degree. First, we define the grain price fluctuation rate as situation indictor and its warning line in a systematic way. Second, we analyze the factors that influence grain price and divide them into eight categories. Third, basing on above result, we select 23 indictors as warning omens. Meanwhile, a new method is attempted to be used in this paper and the grain price early-warning problem is transformed into machine learning problem by introducing SVM method which is gaining popularity in machine learning field at present in the world.