China’s information technology development is rapid; the organic combination of science and technology and education has promoted the reform of the education system; the education platform supplemented by mobile devices provides students with a new learning mode; student learning is no longer limited by the traditional education system; and learners can use mobile terminals for online learning, self-control learning steps, arrange learning time, and be able to combine their own deficiencies to strengthen learning content. Therefore, this paper discusses the design of English education teaching system in the Internet of Things technology environment, in order to provide students with a good learning environment and improve students’ English integration strength. This paper analyzes the research status of mobile learning at home and abroad, expounds the construction principles of mobile English learning system, and designs and studies the overall structure and functional modules of the system. The application of the system can replace the traditional English learning method and can enable students to improve their English ability through mobile devices. In view of the problems of poor teaching effect and high energy consumption in the traditional Internet of Things education platform, this paper puts forward a teaching effect evaluation method of Internet of Things education platform based on long-term memory network. By analyzing the current situation of the Internet of Things education platform, including its development and structure, this paper constructs the evaluation model of the combination of long-term memory neural network model and gray model and realizes the evaluation of the teaching effect of the Internet of Things education platform. Finally, through the study of the model, the teaching effect of the Internet of Things education platform is evaluated. The experimental results show that when the method is used to evaluate the teaching effect, the operating energy consumption accounts for 84% of the total energy consumption of the system, which proves the effectiveness of the method.