Abstract: With the development of computer science and communication network, the scale of the strategic Internet is growing larger with the emergence of more network applications. Owing to the simple function, complex operation and lower efficiency, the old network troubleshooting system already cant satisfy for the demands of carrier development. Put forward in this paper is RSNN algorithm, a design fault diagnosis method, which tightly combines neural network and rough sets. Reduced information table can be obtained, which implies that the number of evaluation criteria is reduced with no information loss through rough set approach. And then, this reduced information is used to develop classification rules and train neural network to infer appropriate parameters. The rules developed by RS-neural network analysis show the best prediction accuracy, if a case does match any of the rules. Its capable of overcoming several shortcomings in existing diagnosis systems, such as a dilemma between stability and redundancy. Since the essence of fault diagnosis is a kind of mapping, an artificial neural network model is adopted to deal with the mapping relation, categorizing the network faults. The experiment system implemented with this method shows a good diagnostic ability.