Short text research has been a hot topic in the field of natural language processing. Due to the sparseness of short texts and serious colloquialisms, its clustering model has the problems of high dimensionality, poor focus of theme, and unclear semantic information. In view of the above problems, this study proposes a short text clustering algorithm with improving the feature weight. Firstly, the rules of multi-factor weight are defined, the comprehensive evaluation function is constructed based on part-of-speech and symbolic sentiment analysis, and the feature words are selected according to the relevancy between the term and the text content. Then, a word skip vector model (continuous skip-gram model) trained in large-scale corpus to obtain a word vector representing the semantic meaning of the feature words. Finally, the RWMD algorithm is used to calculate the similarity between short texts and the K-means algorithm is used to cluster them. The clustering results on the three test sets show that the algorithm effectively improves the accuracy of short text clustering.