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The system of petrochemical process is complex, its variables are various, and their relationship is strongly nonlinear, so use support vector machine to identify the pattern of the fault state on the regenerative subsystem of the catalytic cracking unit in oil refineries. Taking into account the parameters C、sigma have a great influence on the classification accuracy, so an improved genetic algorithm is used to optimize the parameters; And chose decision tree algorithm to classify, according to the separation measure between different categories, start training from the more difficult and most difficult classification sample sets, that is the separation measure is minimum, then combine them into a class cluster and put together with other sample sets, find two of the most difficult classification sample sets and combine them again, combine clusters like this and get the training model finally. The results show that after using the improved genetic algorithm to optimize penalty factor C and the kernel function parameter sigma, the time of classification is shortened and the accuracy of classification is improved. The algorithm based on decision tree support vector machine effectively solve the problem that area cannot be identified that exists in one against one and one against other multiple classification algorithm, can identify the fault types well and has a significant guidance on the fault diagnosis of FCCU.