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In today’s globalized world, cross-cultural communication skills are crucial for English learners. However, traditional English teaching often overlooks cultural differences among students, leading to difficulties in practical communication. The development of deep learning technology has provided new possibilities for English teaching, which can analyze students’ cultural background and learning needs through intelligent systems, and provide more personalized teaching plans. This study focuses on the application of intelligent system in English teaching from the perspective of intercultural education. By analyzing the characteristics of cross-cultural education and the advantages of deep learning technology, an intelligent system model of English teaching based on deep learning is proposed. The model can provide individualized English teaching programs according to students’ cultural backgrounds, learning needs and learning progress. A recommendation algorithm based on a cross-cultural education model comparison is proposed. For each student, retrieve similar teaching cases from the database based on their cultural background and learning needs. Compare the similarity between current students and cases in the database using similarity calculation algorithms. Based on the sustainable development teaching model of college English, the MAP algorithm is analyzed. The experimental results show that the intelligent English teaching system based on deep learning has a remarkable effect on improving students’ English achievement and intercultural communication ability. The model is trained and tested by the measured data. The error of the test results is less than 5%. The results of the experiment prove that the construction of a Chinese college English sustainable development education model can effectively enhance China’s cultural soft power.

In the era of big data, student learning data have become an important basis for evaluating teaching effectiveness and guiding teaching direction. However, in the current English teaching process in many universities, there is still insufficient collection, organization, and analysis of student learning data. This makes it difficult for teachers to comprehensively and accurately grasp the learning situation and needs of students, thus being unable to provide targeted teaching guidance. Data mining technology provides teachers with the possibility of gaining a deeper understanding of student learning by collecting and analyzing a large amount of teaching data. The intelligent CAI system can automatically adjust teaching content and difficulty based on students’ English proficiency and learning progress, providing tailored learning resources for students. This study proposes an innovative English teaching model based on data mining and intelligent CAI. By collecting learning data from students, including learning duration, grades, interactive behavior, etc., data mining algorithms are used for in-depth analysis to reveal their learning characteristics, difficulties, and interests. These analysis results not only help teachers better understand the learning status of students, but also provide strong support for optimizing teaching content and personalized teaching. Meanwhile, this study also focuses on the application of intelligent CAI technology in college English teaching. The intelligent CAI system provides personalized learning resources and real-time feedback to students by simulating the teaching behavior of human teachers. The system can intelligently recommend learning materials and arrange learning plans based on the learning situation and needs of students, and provide timely guidance and assistance when students encounter problems. This personalized learning approach can stimulate students’ interest in learning and improve learning efficiency.

Based on the philosophy of Computer-Supported Cooperative Work (CSCW), the intelligent multimedia teaching platform intends to make it possible for teachers and students to work together effectively over long distances. Music education has long been an important aspect of China’s higher education, and the Corresponding music curriculum structure has evolved over time. As a result of this system, China’s music education has become more systematic and standardized. This has resulted in a plethora of talented musicians for our country. What kind of instructional strategies should be implemented in the classroom? This has been the primary focus of our research efforts thus far. The usage of mobile intelligent terminals in university classrooms has become increasingly powerful as the popularity of these devices and the breadth of the Android platform’s application grows. Student practice and self-evaluation are both made possible by Android’s intelligent platform, allowing students to practice and self-evaluate when they are in class or at home, helping them to better understand their strengths and weaknesses. In this paper, a framework for a music-teaching system has been developed on the server data release and mobile front-end functional view. There is a data layer for the front end, and then there is a data layer for data processing, service, and display. The experimental results show that the proposed framework system has better application performance than the traditional methods.

In today’s information age, the ability of intelligent sharing and scheduling of college English translation corpus resources needs to be improved. Therefore, a method based on fuzzy autocorrelation statistical feature analysis is proposed. First of all, a model must be constructed to detect the semantically relevant dimensional features of college English translation corpus resources under the background of informatization, and to analyze the essential attributes of translation activities by using the hierarchical parameter detection method of translated texts in the narrative structure. Then, a quantitative difference coverage model of word clusters of different lengths is established, with lexical attribute extraction and statistical examination of these resources being performed via a similarity attribute extraction technique for high-frequency word clusters. Subsequently, a semantic dynamic attribute analysis model is developed to derive statistical attributes of college English translation corpus resources within the informatized context. Ultimately, based on the obtained attribute extraction results, a fuzzy autocorrelation statistical attribute analysis method is employed for clustering large datasets. Furthermore, an intelligent particle swarm optimization algorithm is implemented to extract and disseminate lexical attributes of college English translation corpus resources within the information-driven context, so that the college English translation corpus resources can be optimized under the information background. According to the simulation results, this method has excellent accuracy in extracting and sharing lexical features of translated texts, and its feature discrimination ability is also good. It can indeed improve the ability of extracting, sharing, and detecting lexical features of translated texts from college English translation corpus resources.

Blended teaching is a kind of teaching that combines online teaching with traditional teaching, which is defined as “online and offline”. Through the organic combination of these two teaching forms, students’ learning can be from shallow to deep. Therefore, based on the data mining algorithm, this paper designs the method of College English online and offline blended teaching effect. First, it collects the College English blended teaching resources, then builds the College English online and offline teaching support, debugs the College English teaching environment, and finally designs the College English blended teaching model based on the data mining algorithm, so as to realize the College English online and offline blended teaching, The experiment shows that the method designed in this paper can effectively improve the reading ability of College English, and has certain application value.