In the field of natural language processing, traditional information extraction methods involve lexical and syntactic analysis to extract words and parts of speech from sentences to establish semantics. This development of the new artificial intelligence branch makes it suitable for automatic tracing and analyzing blackouts in the power systems, which is very costly to society. Therefore, the purpose of this thesis is to develop a model for extracting useful information from texts about the power industry to conduct an effective blackout analysis. To achieve this goal, we proposed a combined traditional rule-based and machine learning approach. A critical step was to build training data and clean data.