传统的诊断方法效率低又耗费人力,具有较大的局限性。本文提出的基于Adaboost的网络设备故障诊断算法是一种机器学习算法,通过选择C4.5决策树弱分类器集成一个强分类器,从而具有效率高,分类效果等特点。通过与神经网络、KNN等方法进行实验结果比较,所提算法效率更优,能更好应用于网络设备的故障诊断。 Traditional diagnostic methods are inefficient and labor-intensive, with great limitations. The Adaboost-based network equipment fault diagnosis algorithm proposed in this paper is a machine learning algorithm, which is characterized by high efficiency and classification effect by selecting C4.5 decision tree weak classifier to integrate a strong classifier. Compared with the neural network and KNN, the proposed algorithm is more efficient and can be better applied to the fault diagnosis of network equipment.