Choosing the statistical model is the key problem in statistical parsing. Statistical model lies in the core of NLP parsing. This paper investigates 4 primary statistical parsing models, namely PCFG, history-based model, cascading parsing model and head-driven parsing model, and compares their performances in a 10000 Chinese treebank. The analysis based on the experiment were shown in the paper. The comparative study of these models can be exploited to build the practical and effective Chinese parser.