Electric vehicle maintenance standards constitute a key knowledge base for understanding the technical specifications, operational procedures, safety requirements, and quality evaluation systems of electric vehicle maintenance. To systematically reveal the standardization status, technical priorities, and development trends in electric vehicle maintenance field, this study focuses on maintenance standard texts and conducts an in-depth analysis of 35 electric vehicle-related standards published in China from 2015 to the present using text mining methods. First, natural language processing techniques are adopted to preprocess the collected electric vehicle maintenance standard text data. Then, term frequency statistics and TF-IDF keyword extraction are performed to reveal the core concerns of the standard texts, such as energy technologies, system engineering and electrical safety. Simultaneously, large language models are utilized to conduct text segmentation, topic sentence identification, semantic compression, and summary optimization of the standard texts, achieving efficient concentration of standard content. Furthermore, this study employs the Latent Dirichlet Allocation model to deeply explore the core modules and technological evolution trends in the current electric vehicle maintenance standards field. Through text mining methods applied to electric vehicle maintenance standards, enterprises can enhance maintenance service quality based on standards while better grasping the key priorities and trends of industry development.