At present, the majority of English language learners use computers as an aid to support their learning. However, the existing software is relatively homogeneous, which can only correct the pronunciation errors of the English language. Due to the massive number of problems in the software itself, it is difficult for English language learners to correct the errors that occur in their pronunciation properly. In this study, a scoring mechanism is established from the game theory perspective of language intelligent development in combination with multimedia-assisted teaching from three perspectives, that is, acoustics, rhythm, and sense of speech. The output of deep learning is simulated by using network parameters based on language intelligent development to assess the language. Meanwhile, the teaching data and materials for the English language are uploaded and answered online in real time. In this way, students can have access to the course content shared by the teacher, which has a certain auxiliary effect on the English language learning of the students. With the aid of multimedia technology, an excellent English teaching model can be used to enhance the English language learning ability of students effectively and improve their interest and initiative in English learning with the English learning of students as the main body. It can be seen from the results of the simulation that students’ learning efficiency in exploring English language independent learning can be improved effectively mainly by making use of the reference database and aligning it with expert knowledge for error identification.

There were a lot of multisource data and heterogeneous devices in the intelligent system of the Internet of things, and the existing methods were difficult to meet the service needs of users for intelligent entities. Therefore, this paper proposed a semantic model construction method of the Internet of things based on intelligent translation and learning. Firstly, on the basis of summarizing the relevant theories of semantic Internet of things, this paper analyzed the semantic data and its characteristics, and expounded the common ontology matching methods. Secondly, according to the characteristics of service ontology and user ontology in intelligent Internet of things system, a method of matching two different ontologies based on string and semantic relationship was proposed, and the cyclic neural network method was used to organically integrate the semantic data of ontology. Finally, in order to realize the perception and representation of the context information of the Internet of things, a semantic model of the Internet of things based on intelligent translation and learning was constructed. Through experimental comparative analysis, the results showed that compared with the traditional methods based on semantic similarity and semantic distance, the semantic model of the Internet of things proposed in this paper had better performance in accuracy and recall, and can achieve better application effect of the Internet of things system. The model proposed in this paper will provide a theoretical reference for further exploring the sharing and service of heterogeneous devices and data in the intelligent Internet of things system.