Traditional topic models rely largely on word co-occurrence patterns to generate text topics. The data sparseness of short texts due to insufficient context has restrained traditional topic models from achieving good results with regard to short texts. On this basis, this study proposes a short text topic model based on semantic enhancement. The algorithm integrates the Dirichlet Multinomial Mixture (DMM) model with a word embedding model. It obtains the vector representation of words by training global word embedding and local word embedding and calculates the semantic correlation between word vectors with cosine similarity. Besides, it enhances the semantic meaning of words by calculating the weight of topic-related words. Experiments demonstrate the proposed model is more accurate in consistence of topic representation and improves the classification accuracy of the model in regard to short texts.