In order to detect and track interesting topics from massive forum data,firstly,LDA (Latent Dirichlet Allocation) topic model is used to reduce the dimensionality of text data from word space to semantic space.Seco- ndly,a clustering algorithm is employed to cluster the forum data in semantic space.Then,a detection method is proposed to obtain hot topics on the basis of which HTOLDA (Hot-Topic OLDA) topic model is proposed on the basis of OLDA (Online LDA) topic model,which performs priori delivery by choosing hot topics and sets semantic distance on the same topic of adjacent time slices to judge topic status.Experimental results show that HTOLDA topic model is superior to OLDA topic model in terms of modeling each time slice,and that it evolves and tracks the hot topics in online forums effectively.