In recent years， vision-language-action （VLA） models have attracted significant attention in the field of embodied intelligence. As model scale continues to grow， their ability to generalize across complex tasks has steadily improved. However， such performance improvements rely heavily on the availability of large-scale， high-quality training data. Unlike natural language processing and computer vision， which can directly leverage massive internet data， data collection in embodied intelligence typically involves physical interactions between real robots and their environments， leading to high collection costs and complex acquisition processes. Efficiently obtaining， processing， and organizing such data has therefore become a critical challenge for advancing embodied intelligence. To address this issue， this paper provides a systematic review of data collection and processing methods in embodied intelligence. First， we summarize the major data acquisition paradigms from the perspective of data sources and collection strategies， and analyze their characteristics and limitations in terms of data quality， scalability， and collection cost. Second， we present a standardized processing pipeline for embodied intelligence datasets， focusing on key technical components such as action representation alignment， multimodal temporal synchronization， language semantic normalization， and data quality control. Finally， we discuss the evolving data ecosystem in embodied intelligence， highlighting current challenges and potential future directions. The analysis presented in this paper aims to provide insights for dataset construction and large-scale robot learning research in embodied intelligence.