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Novel prediction methods using artificial intelligence have been developed to improve the identification, discovery, and utilization of new types of mineral resources at new depths or using new technologies. However, most artificial intelligence methods require large training data sets that are often unavailable for mineralization prediction models, leading to inaccuracies. To address this issue, we developed a semi‐supervised machine‐learning method to identify metallogenic anomalies using the density‐based spatial clustering of applications with noise method and autoencoder. The outputs of this method show irregularity in distributions inferred from geological, geochemical, and hyperspectral remote sensing data that match known mineralization locations. We focus on the Daqiao mining area of Gansu Province in China to show that the model predictions are highly consistent with known deposits of the Yinmahe and Daqiao gold mines, and two new prospecting areas have been highlighted for further field confirmation. The accuracy of this semi‐supervised learning method was verified by an interdisciplinary intelligent analysis, showing that this method could have wide‐reaching applications for improving regional geological surveys.

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In order to achieve the safe and rapid construction of tunnels under complex geological conditions, it is necessary to fully and timely grasp the key information to carry out effective safety control. Massive information and multi-source heterogeneous data have become the bottleneck for timely and accurate risk assessment. Thus, the utilization of advanced information technologies such as the Internet of Things and data fusion is one of the important ways to improve the safety risk control level of subway tunnel construction. Building risks have a prominent role. This paper builds a tunnel risk information database and uses data mining technology to realize the structured expression of tunnel multi-source structural safety information data and apply it to engineering practice with good results.