This special issue titled “Artificial Intelligence and Big Data Geoscience” consists of 17 papers covering topics such as knowledge graphs, deep learning-based image recognition, machine-readable expression of unstructured geological information, big graph data and community detection, association rule algorithms, 3D geological simulation and mineral prospecting, and the Internet of Things and online monitoring systems. A progressive multi-granularity training deep learning method is proposed for mineral image identification; the model achieves 86.5% accuracy on a commonly used dataset comprising 36 mineral types, increasing the accuracy of mineral identification. Knowledge related to porphyry copper ore in the Qinzhou-Hangzhou mineralization belt, South China, is collected using both primary and literature data sources, and Natural Language Processing (NLP) techniques are used to semantically correlate and reason over the knowledge graph, enabling automated knowledge extraction and reasoning. The association rule algorithm is used to analyze the correlation between trace elements and gold mineralization in major Carlin-type gold deposits in the “Golden Triangle” region of Yunnan-Guizhou-Guangxi provinces, China, and combined with the migration and enrichment law of elements to analyze the genetic mechanism of deposits. By builing a quantitative prospecting indicator method based on association rule algorithm, this study provides new ideas for establishing quantitative prospecting indicators for other types of deposits. In study of machine-readable expression of unstructured geological information and intelligent prediction of mineralization associated anomaly areas in Pangxidong District, western Guangdong, China, unstructured geological information such as stratigraphy, lithology and faults is processed by machine-readable conversion, and two machine learning algorithms—namely, One-Class Support Vector Machine and Auto-Encoder network—are applied to mine the geochemical test data of the stream sediment as well as the comprehensive geological information such as faults and stratigraphy, to extract the features of the mineralizing anomalies, and ultimately realize the intelligent circling of mineralizing anomalous areas. In study of networked monitoring of urban soil pollutants and visualized system based on microservice architecture, a system capable of real-time online monitoring, processing, and analyzing urban soil pollution data to enhance the timeliness of predictions and warnings is developed, where the integrated monitoring and data visualization system is based on the microservices framework Spring Cloud Alibaba. The above mentioned studies provide highly valuable application scenarios and research cases, reflecting to some extent the latest research advances in the field of artificial intelligence and big data geoscience in China, and are worthy of peer attention.