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In order to realize effective management of user tasks in the large computer group, and conquer the defects of the low universality of the given parallel computing framework, a parallel computing framework is propoesd based on improved Quantum particle swarm algorithm and Map-Reduce model. Firstly, the classic Map-Reduce model and the parallel computing flow were described. Then the improved Map-Reduce model was designed based on improved Quantum particle swarm algorithm, the multi-population was parallel searched and the fitness was computed, and the particle was sorted and the particle population was divided, then the control coefficient and particle position were renewed in the Reduce stage, when the global solution was unchanged, the particle was changed by chaos interrupt. The simulation experiment shows the method in this paper can execute task effectively, and compared with the traditional Map-Reduce model it has the less execution time. Therefore, the method in this paper has strong feasibility and universal parallel computing model.