This paper introduces the application and development of intelligent education technology. The algorithm of adaptive mutation, which is based on the traditional genetic algorithm, is proposed, and various mutation probabilities are employed during the calculation process at the right time. The genetic algorithm of adaptive mutation is used to optimize the initial weights and thresholds of the BP neural network. Then, the data-based entropy value method is introduced. The a priori guidance samples are obtained through the entropy value method. Then the adaptive mutation genetic algorithm is used to optimize the BP neural network model to learn the knowledge of the a priori samples. The teaching quality evaluation model based on the combination of the three algorithms is established, which completes the improvement of the existing English teaching quality evaluation system for a certain higher-level job. The English classroom teaching quality assessment system in this paper resembles the expert assessment scores. The implementation also prompted teachers to participate in learning seminars to learn advanced teaching concepts. It shows that the assessment results of the teaching quality assessment system proposed in this paper are reliable and can motivate teachers to work towards the goal of improving teaching quality.