In today’s era of rapid development of educational informatization, improving teaching quality has become the key to the reform and development of the educational field. However, traditional teaching quality evaluation methods often have problems, such as solid subjectivity and insufficient real-time, which are challenging to meet the needs of modern education development. Therefore, this study proposed a real-time evaluation and optimization model of teaching quality, which combines improved D^* and RRT algorithms. Based on big data analysis technology, this model combines the dynamic education quality planning ability of the D^* algorithm with the fast search characteristics of the RRT algorithm, aiming at realizing real-time monitoring, evaluation, and optimization of teaching quality. The model can provide real-time feedback on the teaching effect and provide a scientific basis for educational decision-makers through a comprehensive analysis of several indices in the teaching process. In the experimental part, we selected the actual teaching data of a university as the research object and compared and analyzed the performance of traditional evaluation methods and the model proposed in this study in terms of evaluation accuracy, real-time, and guidance. The results show that the evaluation accuracy of this model is 15.6% higher than that of traditional methods. The real-time performance is 21.8% higher, and it shows significant advantages in the guidance of teaching optimization suggestions. This research result provides a more accurate and efficient teaching quality evaluation tool for educational administrators. It has important practical significance and application value for promoting education and teaching reform and improving teaching quality.

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