Abstract: A quantum dingo optimization algorithm（QDOA）based on quantum behavior and Lévy flight was proposed to address the limitations of traditional dingo optimization algorithms in terms of search scope and the fact that the search results were often not globally optimal.Quantum behavior gave uncertainty to the trajectory and velocity of the population,so that the search range of the algorithm could cover the whole feasible space.The random step size of Lévy flight strategy overcomes the problem that the algorithm was easy to fall into local optimum in the later stage of iteration,and improves the accuracy of solution.Through the performance test of the benchmark function,QDOA performed better than other algorithms in accuracy and precision.QDOA was used to identify the field data of high load section,medium and low load section of selective catalytic reduction（SCR）denitration control system of 660 MW coal-fired unit in a power plant in Ningxia. The transfer function between valve opening and inlet ammonia flow,inlet ammonia flow and outlet NOxconcentration of SCR denitrification system was established. The identified model could better reflect the dynamic characteristics of the SCR denitration control system,which proves the feasibility of the algorithm.