Abstract
<jats:p>With the rapid development of the rail transit industry, the demand for professional talents is increasing, and traditional teaching methods are difficult to meet the needs of modern rail transit education. This study proposes a rail transit teaching behavior recognition and learning path optimization method based on intelligent perception, aiming to improve the teaching quality and learning effect. This method uses multimodal sensors to collect the behavioral data of teachers and students in the teaching process, and identifies and analyzes the teaching behavior through deep learning algorithms. At the same time, combined with the learning performance and personal characteristics of students, an adaptive learning path optimization model is constructed. The experimental results show that this method can effectively identify the teaching behavior pattern of teachers with an accuracy rate of more than 95%; at the same time, the optimized learning path can significantly improve the learning efficiency of students, and the average score is improved by 15%. In addition, this study also explores the application prospects of this method in virtual simulation teaching and distance education, providing new ideas and technical support for the innovative development of rail transit education.</jats:p>