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How to quickly and efficiently get real-time emergency data from a massive network is becoming a major problem in monitoring internet public opinion. In this paper, we propose a clustering algorithm based on simplified cluster hypothesis to improve system efficiency. Meanwhile, by analysis of the emergency's characteristics under the dynamic web environment, we propose a multi-strategy quantitative analysis model for network emergency by researching on the following aspects: (1) Using field feature vector similarity to dividing emergency category. (2) Using Sum Limit method to calculate the emergency's maximum effect range. (3) Using distribution to calculate the emergency's timeliness. Fi- nally, through analysis dynamic data from Tianya.cn, the experimental results prove the model is effective.

A Multi-Strategy Quantitative Analysis Model for Network Emergency