High-performance computing clusters are mainly used to deal with complex computing problems and are widely used in the fields of meteorology, ocean, environment, life science, and computer-aided engineering. Language is the way humans communicate and communicate. Linguistic features are the stylistic features that distinguish all languages from other languages. This paper aims to study how to analyze English language features based on high-performance computing. This paper addresses the problem of linguistic feature analysis, which is built on high-performance computing. Therefore, this paper expounds the related concepts and algorithms, and designs and analyzes the characteristics of English language. The experimental results show that among the 160 English sentences in two different journals, complex sentences are the most used, with a total of 55 sentences, accounting for 34.38%. The second is mixed sentence types, 47 of which are mixed sentence structures, accounting for 29.38%. Among them, the combination of simple sentences + coordinating complex sentences + complex sentences constitutes the most mixed sentences, which appear 12 times and 8 times in ELT Journal and SSCI, respectively, accounting for 15.00% and 10.00% of their respective corpora.

In this study, a multidimensional corpus English teaching model is constructed using the data-driven model. This study uses a data-driven collection of massive amounts of data to generate a multidimensional corpus. The data-driven generation of a multidimensional corpus to build a teaching model is studied, and the principle of data driven, the computational process, and the characteristics of the corpus are analyzed. Due to the deficiency of data-driven modeling without correlating process variables with quality variables, this study adopts an artificial intelligence algorithm and analyzes the basic principle, computational process, and advantages and disadvantages of the method. The model is simulated and verified for multidimensional corpus and English teaching. To address the shortcomings of the AI algorithm, which has a complex computation process and no orthogonal decomposition of the data space, the autoregressive latent structure projection algorithm is designed by integrating the autoregressive idea with the artificial intelligence (AI) algorithm. This algorithm can orthogonally decompose the sample data space and simplify the modeling process. Finally, the algorithm is validated by simulation. To verify the results of the teaching model, the fuzzy C-means clustering algorithm is combined with the autoregressive latent structure projection algorithm in this study. The sample data used in the modeling are divided into categories, and the affiliation function is calculated for each category. The affiliation function is used to calculate the affiliation of the online calculation results for each category, and the final evaluation results are obtained based on the fuzzy comprehensive evaluation method. Finally, taking junior students as an example, the simulation is carried out to verify the effectiveness of the English teaching model. The research results show that the corpus-based English flipped classroom teaching model improves English teaching methods, enhances students’ English proficiency and independent learning ability, and provides a practical basis for English teaching model exploration.