The invention relates to an HMM-based part-of-speech tagging method and belongs to the field of information processing technology. According to the method, first, words in a word bank are ordered according to unicodes so that a dichotomy method can be used for quick search during word segmentation; second, an HMM is introduced, a tagged corpus serves as a training set and a test set to be used forobtaining three parameters of the HMM, and therefore a plurality of observable states in the HMM are obtained; third, secondary word segmentation is performed, the words not found in a primary word segmentation result are searched for in the observable states in the HMM, and a maximum entropy model is introduced to perform tagging on new words not found; and last, a viterbi algorithm is used to calculate an optimal hidden sequence of the HMM, and the optimal hidden sequence is combined with the tagging result of the maximum entropy model to obtain a final part-of-speech tagging result. Compared with the prior art, the phenomenon that a single part-of-speech tagging method is low in speed and low in new word recognition rate, and consequently a tagging result is low in accuracy is mainly solved, and the efficiency and accuracy of part-of-speech tagging are improved.