When the traditional topic model method is used to analyze the sentiment of the topic model for short text corpora such as comments in the medical service platform, the problem of poor context dependency may occur. A WLDA algorithm based on word embedding is proposed. The word w* trained in the Skip-Gram model replaces the word w` in the Gibbs sampling algorithm in the traditional LDA model, and the parameter λ is introduced to control the resampling probability of the words during Gibbs sampling. The experimental results show that the subject model has a high degree of consistency compared to similar topic models.