The application of big data mining and machine learning algorithms in mineralization prediction has become an important research trend, but unstructured geological data cannot be directly mined—first they need to be converted to machine-readable expressions. In this study of the Pangxidong ore district in western Guangdong Province, the unstructured geological information such as stratigraphy, lithology, faults are converted into machine-readable format, and two machine learning algorithms, namely, One-Class Support Vector Machine and Auto-Encoder Network, are applied to mine the geochemical test data of stream sediments as well as the comprehensive geological information on faults, stratigraphy, etc. to extract the features of mineralization anomalies and ultimately achieve intelligent delineation of the anomaly areas. Through combined application of One-Hot Encoder and the weighted variable method for spatially weighted principal component analysis, the structural transformation of the unstructured geological information is realized, and geological information is maximally preserved for data mining. It is demonstrated that the application of One-Class Support Vector Machine and Auto-Encoder Network can effectively solve the problem of data imbalance, as the numbers of ore and non-ore spots in the study area are seriously unbalanced. The prediction results generated using the integrated, synthesized multi-source geological data are relatively consistent with the observed spatial distribution of Pb-Zn deposits and the actual geological structure in the study area, indicating the two algorithms can effectively identify potential prospecting targets and ore deposits. Compared with traditional geochemical prospecting methods, the intelligent prediction method can process and integrate multi-source geological information about the ore-forming processes and identify mineralization anomaly areas. This method is applicable in prospecting areas without prior ore discovery, thereby improving the efficiency of ore prospecting and increasing the possibility of finding ore deposits.