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Optimizing the parameters of Supported Vector Machine generally introduces extra variables, causing the original problem more complicated. Hence, a Self-adaptive Simulated Annealing Algorithm is proposed. By means of designing a self-adaptive annealing schedules, the course of searching the best parameters only depends on two variables, annealing rate and grid search granularity, keeping relatively fewer parameters setting. As with the experiment shown on related datasets, ASA algorithm acquires the faster speed of convergence and decent diagnosis accuracy. Utilizing ASA acquires better parameters of SVM ,while improving the accuracy of transformer diagnosis。