Indoor environment quality (IEQ) is one of the most concerned building performances during the operation stage. The non-uniform spatial distribution of various IEQ parameters in large-scale public buildings has been demonstrated to be an essential factor affecting occupant comfort and building energy consumption. Currently, IEQ sensors have been widely employed in buildings to monitor thermal, visual, acoustic and air quality. However, there is a lack of effective methods for exploring the typical spatial distribution of indoor environmental quality parameters, which is crucial for assessing and controlling non-uniform indoor environments. In this study, a novel clustering method for extracting IEQ spatial distribution patterns is proposed. Firstly, representation vectors reflecting IEQ distributions in the concerned space are generated based on the low-rank sparse representation. Secondly, a multi-step clustering method, which addressed the problems of the “curse of dimensionality”, is designed to obtain typical IEQ distribution patterns of the entire indoor space. The proposed method was applied to the analysis of indoor thermal environment in Beijing Daxing international airport terminal. As a result, four typical temperature spatial distribution patterns of the terminal were extracted from a four-month monitoring, which had been validated for their good representativeness. These typical patterns revealed typical environmental issues in the terminal, such as long-term localized overheating and temperature increases due to a sudden influx of people. The extracted typical IEQ spatial distribution patterns could assist building operators in effectively assessing the uneven distribution of IEQ space under current environmental conditions, facilitating targeted environmental improvements, optimization of thermal comfort levels, and application of energy-saving measures.