To solve the problems such as low accuracy and poor interpretability of traditional news topic mining, a new method was proposed based on weighted Latent Dirichlet Allocation (LDA) that combined with the information structure characters of the news. Firstly, the vocabulary weights were improved from different angles and the composite weights were built, the more expressive words were got by extending the process of feature items generated by the LDA model. Secondly, the Category Distinguish Word (CDW) method was used to optimize the word order of the generated result, which could reduce the noise and the ambiguity of the topics and improve the interpretability of the topics. Finally, according to the mathematical characteristics of the probability distribution model of the topics, the topics were quantified in terms of the contribution degree from the documents to the topics and the topics weight probability to get the hot topics. The simulation results show that the false negative rate and false positive rate of the weighted LDA model drop by an average of 1.43% and 0.16% compared with the traditional LDA model, and the minimum standard price drops by an average of 2.68%. It confirms the feasibility and effectiveness of this method.