关键词: LDA 主题模型；汽车智能驾驶技术；热点主题；K-means 聚类；发展路径

Abstract: This study focuses on relevant content in the field of automotive intelligent driving on WeChat public accounts over the past three years, using "automotive intelligent driving" and "automotive industry" as keywords for thematic retrieval. After data cleaning and organisation, 121 high-quality texts were selected as the analysis corpus. The study employs the latent dirichlet allocation (LDA) topic model to train the obtained texts, identifying the core research hotspots in China's intelligent driving technology field, namely computing power, risk, risk level, data models,driver monitoring, assisted driving, sensor fusion, and intelligent vehicles. On this basis, the study further points out that automotive industry practitioners and learners in related fields should pay sufficient attention to eight aspects–computing power research, risk research, risk level research, data model research, driver monitoring research, assisted driving research, sensor fusion research, and intelligent vehicle research–when conducting research in intelligent driving technology in the future. For hotspot theme task identification, the study verifies the effectiveness and practicality of the LDA topic model through K-means clustering. Its analysis results are conducive to researchers and industry personnel quickly grasping frontier developments and research focuses in the field. Additionally, the study's conclusions can provide references for automotive industry policy-making and corporate technology planning, helping the automotive sector form a clearer development path in key intelligent driving technologies.

Key words: LDA topic model; automotive intelligent driving technology; hot topics; K-means clustering; development path