Chinese Segmentation Ambiguity (CSA) is a fundamental problem confronted when processing Chinese language, where a sentence can generate more than one segmentation paths. Two techniques are commonly used to identify CSA: Omni-segmentation and Bi-directional Maximum Matching (BiMM). Due to the high computational complexity, Omni-segmentation is difficult to be applied for big data. BiMM is easier to be implemented and has a higher speed. However, recall of BiMM is much lower. In this paper, a Segmentation Matrix (SM) method is presented, which encodes each sentence as a matrix, then maps string operation into set operations. To identify CSA, instead of scanning a whole sentence, only specific areas of the matrix are checked. SM has a computational complexity close to BiMM with recall the same as Omni-segmentation. In addition to CSA identification, SM also supports lexicon-based Chinese word segmentation. In our experiments, based on SM, several issues about CSA are explored. The result shows that SM is useful for CSA analysis.