Abstract: This paper discussed the theory and the shortage of traditional hidden Markov models, and based on this, the theory and algorithms of the infinite Hidden Markov Model were also illuminated in detail. In iHMM, firstly, the inference of state transition probability was calculated in Dirichlet process. Secondly, a hierarchical Dirichlet process was used to infer hidden state mechanism and the emission mechanism. Lastly, the model hyperparameter optimization and likelihood estimation were discussed. The good performance of the inference algorithm of iHMM is tested and verified through the simulation examples, and the results show that iHMM is equipped with a good ability to explore the number of states, and reflect the state of the sequence of structural features accurately.