In consideration of the principles that attack benefits of network combined targets with targets are maximal and its own consumption is minimal in total, a multiobjective optimization model is established under conditions of network confrontation environment in fire distribution. Under conditions of random network topology introduced, the effect of fire distribution corresponding to the random network is analyzed. This paper adopts quantuminspired immune clonic multiobjective optimization algorithm to solve the model of fire distribution. Though experimental simulation, the change circumstances of the total attack benefits are analyzed by using different cost ammunition. The attack efficiency of the fire distribution scheme increases by 23% by using the improved algorithm over the fire distribution scheme by using standard algorithm. The convergence of the algorithm and superiority of Pareto solution distribution are studied. The experiments demonstrate that the Pareto efficiency solution distribution increases 42% by using the improved algorithm over using the standard algorithm. The superiority of the model and the efficiency of the algorithm are verified.