Because of the sparse and dynamic crisscross characteristics, the short text makes the weight of traditional weighted method difficult to use effectively. This paper presents a new feature weight calculation algorithm based on part of speech. This algorithm is the quantum particle swarm optimization algorithm introduced into translation decision model which can calculate the probability of a feature with certain part of speech. Then it is tested by the text clustering algorithm. The test results show that the improved feature weight calculation algorithm on the clustering accuracy is better than TF-IDF and QPSO algorithm.