Abstract: In the field of forensic science identification, geochemical-related materials such as soils and rocks were important sources of material evidence. In actual case analysis, the information provided by material evidence often pointed to unknown areas. Predicting the source of material evidence was an extremely challenging task when the location of the crime scene was not clear. To address the uncertainty of geochemical material evidence information, a dataset including physicochemical properties and geographic information such as mineral composition, element content, and geographical location was established. By comparing the sample information from the crime scene, the source of the material evidence samples could be quickly determined, providing strong technical and evidentiary support for case investigation. Surface soil samples (0−10cm) were collected from urban areas in Shenyang City, Liaoning Province, and X-ray fluorescence spectrometry (XRF) and X-ray powder diffraction (XRD) were applied to test and analyze 15 elements (SiO₂, Al₂O₃, CaO, K₂O, Na₂O, MgO, TFe₂O₃, Ti, Mn, Ba, P, Zr, Cu, Zn and Pb) and mineral components in the soil material evidence samples. With the help of MapGIS software, element content distribution maps were drawn to explore the characteristics and influencing factors of element distribution in the study area. Principal component analysis (PCA) was used to classify and identify soil samples from three study areas. The results indicated that: (1) through urban geological mapping, accurate and intuitive element content distribution maps can be obtained, allowing court workers to compare the elemental characteristics of soil samples and trace the source of material evidence. (2) The soil material evidence samples from Shenyang were mainly composed of quartz, feldspar, montmorillonite, and illite (88.0%−98.0%). The XRD diffraction bar thermal map facilitated court workers to conduct comparative analysis of large amounts of data. (3) Based on PCA, a dimensionality reduction analysis of 15 elements from three study areas was conducted, and significant regional discrimination of soil samples from the three study areas was achieved within a 95% confidence interval (Group 1: F₁<0, F₂<0; Group 2: F₁>0, F₂>0; Group 3: F₁>0, F₂<0). (4) There were significant differences in chlorite, tremolite, kaolinite, calcite, and dolomite among soil samples from the three study areas, further supporting the accuracy of PCA classification. In summary, the combined application of XRF and XRD technology could be used to effectively distinguish soil material evidence samples from different areas within the city, providing directed research areas for soil material evidence traceability investigation and important clues to narrow down the investigation scope.