为了进一步提高网络蜘蛛在互联网、物联网和实时工业控制网络中信息采集的效率,分析了导致网络蜘蛛陷入局部最优解的原因,将遗传算法引入到网络蜘蛛的应用当中求解全局最优解,针对传统遗传算法中存在早熟和收敛慢的问题对选择、交叉、变异这三种核心算子进行了改进。经实验对比表明,该算法和网络蜘蛛相结合克服了以上问题,具有较高的搜索查全率和搜索准确率。 In order to further improve the efficiency of network spiders in information collection in Internet, Internet of Things and real-time industrial control networks, this paper analyzes the reasons that led to the spider in local optima. By introducing genetic algorithm into the application of network spiders, Aiming at the problems of premature convergence and slow convergence in traditional genetic algorithm, the three core operators of selection, crossover and mutation are improved. The experimental comparison shows that the algorithm and network spider to overcome the above problems, with high search recall rate and search accuracy.