【摘要】 深海热液循环系统是理解地球物质循环与极端生态系统演化的关键环节，然而其特殊的环境条件长期制约了有效观测与深入研究的开展。近年来，机器学习技术在地球科学领域的应用不断拓展，为深海热液系统研究提供了全新的思路。本文系统梳理了机器学习技术在该领域中的主要应用进展，重点包括：分类模型在海底热液喷口识别和基底岩性判别中的应用，预测模型在多金属硫化物分布及热液生物栖息模式分析中的探索，以及图像识别技术在海底影像与生态系统结构处理的研究。在总结既有成果的基础上，本文进一步指出当前研究面临的关键问题，包括深海数据稀缺、样本时空偏差明显、模型泛化能力不足与可解释性有限等。结合地学数据与大语言模型的发展趋势，未来可重点关注：(1)完善深海观测与数据共享机制，提升数据获取效率与质量；(2)开发适配深海复杂环境的专用模型，提升算法适应性与稳定性；(3)推动跨学科融合，构建多源数据驱动的一体化研究框架。更多还原

【Abstract】 Deep-sea hydrothermal circulation systems play a crucial role in understanding the Earth’s material cycling and the evolution of extreme ecosystems. However, their unique environments have long constrained effective observation and in-depth study. In recent years, the application of machine learning techniques in geosciences has been expanded, providing new perspectives for exploring deep-sea hydrothermal systems. This paper systematically reviews recent advances in this area, focusing on the classification for hydrothermal vent detection and basement lithology identification, prediction for polymetallic sulfide distribution, analysis of hydrothermal biological habitat, and image recognition of subsea imagery, and ecosystem structure investigation. Additional to significant achievements made, challenges in data scarcity, spatiotemporal sampling bias, limited model generalization, and poor interpretability remain. To address these issues, future research should focus on improving deep-sea observation and data-sharing mechanisms, developing models specified for the complex deep-sea environment, and promoting interdisciplinary integration to construct a multi-source, data-driven research framework.更多还原