A variety of methods, including Markov chains, multivariate statistics, optimization, system dynamics, and CLUE/CA, have been widely used to study land use change in different areas. Previous studies indicate that these methods obviously have their own pros and cons when they are applied to the studies on land use change. New approaches will probably provide a better alternative if it can assimilate some of the advantages of current available methods.Case-based reasoning (CBR) is an effective method which was widely used to study geographical problems. However, the CBR approach is far from perfect in presenting complicated geographical phenomena, particularly in computing and reasoning the similarity between current study cases to those ones that have been studied. Research is in great need to improve CBR-based geographic information portrayal modeling and reasoning algorithm. This paper reports a CBR-based method, including a spatial relationship extracting algorithm and a model describing the similar reasoning between spatially related cases. These methods were tested by examining the land use change in Zhuhai City, which is located on the western Pearl River Mouth of Guangdong, China. In order to evaluate the prediction accuracy derived from CBR-based method, we also use Bayesian network method to study land use change in our study area.As the results indicate, both CBR and Bayesian network approaches yielded similar prediction accuracy. However, the advantages in CBR approach are obvious, particularly in dealing with complicated geographic phenomena. When using the CBR method, it is unnecessary to define those complicated conversion regulations. Instead, the method predicts land use change simply based on knowledge retrieved from old cases, hence significantly improving the efficiency in building the case library, as well as case querying in the library. By contrast, Bayesian networks require extensive computation and more unrealistic assumptions, i.e., complete dataset, no preferred selection, and non-continuous variables.