There are various approaches used to study land use change (LUC), such as regression analysis, probability statistics, system dynamics, cellular automata and Bayesian network. These approaches have their specific characteristics and practical applications in the LUC. Although there were some researches revealing the advantages and disadvantages of some approaches, there were relatively few studies on the suitability of these approaches. This study focused on the suitability of CBR approach for LUC estimation, on the basis of the CBR model for the LUC estimation. The comparison experiments were conducted from three aspects, selection approach of the test cases, selection of variables and weights of the variables, to explore the influences of these factors on the estimation accuracy of LUC. The land use changes in Zhuhai region, China during 1995-2000 were used as a case study to conduct the comparative experiments. The concrete comparison strategies include: (1) To choose the test cases by selective approach and stochastic approach to explore the effects of the selection approach on the LUC estimation accuracy; (2) to neglect different variables in turn representing three categories of impacts respectively to explore the effects of the neglect of the variables on the estimation accuracy; (3) to change the weights of variables in turn to explore the effects of the weights of specific variables on the estimation accuracy. The experimental results are shown as follows. Firstly, the selection approach of test cases has insignificant effects on the LUC estimation accuracy under the circumstance that the historical cases are abundant. Secondly, the neglect of the ordinary variables has insignificant influences on the estimation accuracy on the condition that vital variables are selected. Thirdly, the weights of the ordinary variables have insignificant effects on the estimation accuracy in the event that greater weights are assigned to the vital variables. These results demonstrate that CBR is an effective method for solving LUC problems with the advantages of simple construction, wide application, high accuracy and stable pattern. The stability of the LUC estimation accuracy based on CBR approach can be kept on the condition of plentiful historical cases when vital variables are selected and higher weights are assigned to them. In this case, CBR method shows a good suitability for LUC estimation. In addition, the incorporation of the new component“geographic environment”into the CBR model efficiently improves the estimation accuracy of LUC.