There is an issue with inappropriate assessment, despite the fact that teaching quality evaluation is very crucial in higher vocational preschool education. It is challenging to evaluate students’ learning thoroughly and properly using traditional assessment approaches since they often depend on subjective judgment and single indications. In order to enhance the quality of instruction and the overall development of students, this paper suggests a BP neural network algorithm. The algorithm makes use of its robust data analysis and pattern recognition capabilities to create an evaluation system for higher vocational preschool programs. The significance of higher vocational preschool education is firstly examined using the theory of educational data analysis. In order to eliminate interference factors in evaluating teaching quality, the indicators are classified according to the needs of preschool education. The theory of educational data analysis then proceeds to assess the requirements of the system for evaluating teaching, develop the architecture of the system, create the curriculum for preschool, and conduct a thorough analysis of the findings of the assessment of teaching quality. Findings demonstrate that BP neural network-based assessment system for higher vocational preschool education teaching can reliably assess students’ progress and provide useful guidance to educators in making informed decisions about their students' education. The goal of using this method is to make higher vocational preschool instruction more effective and efficient.