【Objective】 For the rational use of land resources, it is important to obtain accurate spatial distribution of soil types using digital soil mapping technologies. 【Method】 In this study, environmental factors were screened according to the soil parent material type based on field sampling points, and then three different mapping methods, random forest, SoLIM, and KNN, were used to map the zones according to the selected environmental factors, respectively. Each method was used individually to generate zoning maps, providing different reasoning for the spatial distribution of soil types. The zoning mapping results were obtained and combined to form a universal spatial distribution map of soil types, and then, we used the FP-Growth algorithm to effectively mine the internal correlation between environmental factors. By combining these associations with different mapping results obtained previously, the spatial distribution of soil types in the study area was deduced and used to obtain higher quality and precision inference results. 【Result】 The mapping results revealed several key findings: (1) The independent mapping of soil type based on the parent material type of soil by three different mapping methods is more effective and accurate than the joint mapping of all parent materials, and the inference of spatial distribution of soil types is also more reasonable. (2) Among the three mapping methods adopted in this study, the method combining random forest and frequent itemset mapping had the highest accuracy of 70.73%. Moreover, the results obtained by this combined method are similar to the spatial distribution of soil types inferred by the other two combined methods. Through comparative analysis, we were able to determine the approximate spatial distribution of soil species in the study area. (3) After the three mapping methods were combined with frequent itemsets, we observed that all methods had different degrees of improvement in accuracy verification and Kappa coefficient. Among them, the KNN method had the most significant improvement effect, the total mapping accuracy increased by 9.76%, and the Kappa coefficient increased by 11.70%. On the contrary, the random forest method had the smallest improvement, wherein, the total mapping accuracy and the Kappa coefficient increased by 4.88%, and 5.85%, respectively. These results validate the effectiveness of the combination method designed in this study. 【Conclusion】 The first, aspect of this study aimed to investigate the influence of soil parent material type on environmental factor screening. This aspect had relatively important reference significance for selecting appropriate environmental factors in the process of digital soil mapping. On the other hand, by combining frequent itemsets with the three different mapping methods used, this study not only provides a new method and idea for the exploration and application of digital soil mapping, but also provides a useful reference for the information application of frequent itemsets association.