This paper proposes a novel Chinese syntactic parsing model based on semantic class, which is a variant of normal lexicalized statistical model. It attempts to make use of the syntactic and semantic similarity between Chinese words and then produces a more knowledgeable estimate of the probability of grammar rules. A simple but effective unsupervised method is designed to determine the proper semantic class of given words. Semantic class is used to improve the performance of parsing model. We evaluate our methods on the widely used Penn Chinese Treebank. Experimental results show that it outperforms a famous lexicalized model significantly on appropriate semantic class levels.