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This research focused on the classification of SQL server data mining for fault diagnosis of complex dynamic system model, and carried out the optimization of parameter on SQL data mining classification algorithm, which taking parameter optimization decision tree as an example. There is a problem in data mining that the parameters settings are chosen by manual and the accuracy of prediction is unsatisfactory. In addition, parameter optimization with simple GA algorithm is prone to oscillation and prematurity. For low accuracy of artificial parameters settings and unsatisfactory results, an improved annealing genetic algorithm in SQL server data mining has been proposed to optimize the parameter of decision tree. Moreover, what have been achieved are that the global search and fast convergence to the global optimal solution.