Topic tracking is a task in research on identifying, mining and self-organizing relevant information to news topics. Its key issue is to establish statistical models that adapt the kind of news topic. This includes two aspects: one is topical structure; the other is topic evolution. This paper focuses on comparing and analyzing the features of three main kinds of topic models including words bag, hierarchical tree and chain. Different performances of static and dynamic topic models are deeply discussed, and a term overlapping rate based evaluation method, namely descending kernel track, is proposed to evaluate the abilities of static and dynamic topic models on tracking the trend of topic development. On this basis, this paper respectively proposes two methods of burst based incremental learning and temporal event chain to improve the performance of capturing topic kernels of dynamic topic models. Experiments adopt the international-standard corpus TDT4 and minimum detection error tradeoff evaluation method proposed by NIST (National Institute of Standards and Technology), along with descending kernel track method to evaluate the main topic models. The results show that structural dynamic models have the best tracking performance, and the burst based incremental learning algorithm and temporal event chain achieve 0.4% and 3.3% improvement respectively.