Geochemical data are important part of applied geochemical research and basic achievements of geochemical exploration survey. Exploration geochemical data are mainly expressed in the form of “percentages of element mass concentration (abbreviated to “concentration”)”, which are typical compositional data. It expresses information on relative mass contribution about the ratio of “parts to whole”, rather than information on absolute mass change. The concentration data are distributed in the simplex space, rather than the entire Euclidean space. Before data processing, application of appropriate logarithmic ratio transformation would improve the structure and information representation of compositional data. In this paper, the Pb concentration data in the soil of the Zhaojikou Pb- Zn deposit in the Anhui Province is taken as a case study. The logarithmic ratio transformation methods were used to optimize the data structure of the Pb concentration to improve the expression of relative information. Then the K- means clustering of unsupervised learning methods was adopted to identify the background and abnormal information according to the distances of the centroids of the distribution space of the logarithmic ratio transformation data. Finally, the background and anomaly identified by the K- means clustering method was compared with the results of iterative 2δ method and the concentration- area fractal analysis method to evaluate its performance. The results show that: ① the log- ratio method can effectively improve the structure and information expression of concentration data; ② K- means clustering method can effectively identify the background and abnormal information of log- ratio transformed data; its performance is similar to the concentration- area fractal analysis method, and better than the iterative 2δ method.