Hidden Markov model is one of important approaches for text information extraction.In the first-order hidden Markov model

there is the hypothesis that the transition probability of state and the output probability of observation are only dependent on the current state of the model

which debases the precision of information extraction comparatively.The relationship between the probability and the model’s historical states is considered reasonably in the second-order hidden Markov model which has stronger performance of recognition for incorrect information.An algorithm of text information extraction based on the second-order hidden Markov model is proposed.The validity of the second-order hidden Markov model in information extraction is analyzed.Simulation Experiments show that the new algorithm has higher precision than the algorithm based on the first-order hidden Markov model.