Research and Implementation of Real-time Automatic Web Page Classification System

With the development of Internet and communication technology, the Internet data growth rapidly, and the type of network services varied. According to the different properties of the network services, network services classification is the foundation of many network applications, including network service management, green Internet, network bandwidth usage category management, network reputation management, security filtering and so on. Due to the variety of web content and text length, the traditional classification methods can't effectively solve the problem of large-scale web page classification. In this paper, we design and implement a real-time automatic Web page classification system AWCS, including self-feedback system architecture, multi-dimensional network services classification standard, active and passive combining network service discovery and collection technology, automatic self-correction network service classification techniques. Performance tests show that the classification accuracy of AWCS is significantly higher than the traditional algorithms. This framework offers a promising approach for large-scale real-time network data classification system.