Abstract: The latent Dirichlet allocation (LDA)is a popular three-layer probability topic model,which implements the clustering of words in document and document at the topic level.This model is based on the Bag of Words(BOW) mo-del,and each word has the same importance.It simplifies the complexity of modeling,but makes the topic distributions tend to high-frequency words,which affects the semantic coherence of the topic model.To achieve this goal,an LDA algorithm based on dynamic weight was proposed.The fundamental idea of the algorithm is that each word has different importance.In the iterative process of modeling,word weights are generated dynamically according to the topic distribution of words and feedback to topic modeling,reducing the influence of high frequency words and improving the role of keywords.Experiments on four public datasets show that the LDA algorithm based on dynamic weight can be superior to the current popular LDA inference algorithms in terms of topic semantic coherence,text classification accuracy,gene-ralization performance and precision

Key words: Dynamic weight, Latent dirichlet allocation, Topic model