Luyao Wang

Nanjing Data Association

Abstract：

With the rapid development of information technology, computer networks play an increasingly important role in various industries of society. However, network faults have become a major issue affecting the normal operation of computer networks. Traditional fault diagnosis methods, which rely heavily on manual experience or rule-based diagnostic models, cannot effectively cope with complex and dynamic network environments. In recent years, intelligent fault diagnosis methods based on machine learning algorithms have gradually become a research hotspot. This paper analyzes the limitations of current network fault diagnosis methods and explores the application of machine learning in fault diagnosis. Through the introduction and experimental research of common machine learning algorithms, a machine learning-based computer network fault diagnosis model is proposed, and its performance is evaluated. The research shows that machine learning algorithms can effectively improve the accuracy and efficiency of fault diagnosis, providing strong support for the stable operation of computer networks.

Key Words：

computer network; machine learning; fault diagnosis; intellectualization; algorithm