Dissolved organic matters (DOM) in wastewater are the key fraction to be treated, and affect various technologies inversely. Ultrahigh-resolution mass spectrometry (URHMS) offers a molecular level insight into the DOM transformation during wastewater treatment processes. However, owing to the complex and high dimensional dataset, data mining is the knowledge gap to widely apply molecular information method. This review summarises the framework of data acquisition, data treatment and data mining processes. The data acquisition and treatment include sample pre-treatment, instrumental analysis and raw data conversion. Typical data mining strategies are classified into Venn diagram and correlation analysis, which can be applied in three wastewater treatment scenarios. Venn diagram is expanded to a generalized classification model and semiquantitative calculation methods. Additionally, correlation analysis can link DOM molecules with various parameters, including time, special, environmental and microbial factors. These data mining strategies are suitable for both batch and continuous wastewater treatment reactors in lab or engineering scale. Recommends are proposed to model the DOM transformation in the future. This review offers a framework to support UHRMS applications in wastewater treatment, which can be applied in other environmental monitoring scenarios as well.

废水中的溶解有机物 (DOM) 是需要处理的关键部分，对各种技术产生负面影响。超高分辨率质谱 (URHMS) 提供了对废水处理过程中 DOM 转化的分子水平洞察。然而，由于数据集复杂且维数高，数据挖掘是分子信息方法广泛应用的知识缺口。本综述总结了数据采集、数据处理和数据挖掘过程的框架。数据采集​​和处理包括样品前处理、仪器分析和原始数据转换。典型的数据挖掘策略分为维恩图和相关性分析，可应用于三种废水处理场景。维恩图扩展为广义分类模型和半定量计算方法。此外，相关性分析可以将 DOM 分子与各种参数联系起来，包括时间、特殊、环境和微生物因素。这些数据挖掘策略适用于实验室或工程规模的间歇式和连续式废水处理反应器。建议在未来对 DOM 转换进行建模。本次审查提供了一个框架来支持 UHRMS 在废水处理中的应用，该框架也可以应用于其他环境监测场景。这些数据挖掘策略适用于实验室或工程规模的间歇式和连续式废水处理反应器。建议在未来对 DOM 转换进行建模。本次审查提供了一个支持 UHRMS 在废水处理中应用的框架，该框架也可应用于其他环境监测场景。这些数据挖掘策略适用于实验室或工程规模的间歇式和连续式废水处理反应器。建议在未来对 DOM 转换进行建模。本次审查提供了一个框架来支持 UHRMS 在废水处理中的应用，该框架也可以应用于其他环境监测场景。