Author(s)

Ruichao Li ¹

Affiliation(s)

¹ School of English Language and Culture, Xi'an Fanyi University, Xi'an, Shaanxi, China

Corresponding Author

Ruichao Li

ABSTRACT

This study aims to explore translation optimization method of complex English long sentences by combining natural language information processing with artificial intelligence compressed sensing algorithm. Through the English-Chinese translation experiment of scientific and technological texts in the field of Internet of Things (IoT), the translation effects of the compressed sensing algorithm and the traditional algorithm are compared and analyzed. The experimental and control groups consist of English majors, and five tests are used to evaluate translation accuracy. The experimental results show that the application of the compressed sensing algorithm significantly improves translation quality in the translation of long and difficult sentences. Especially in the Chinese-English translation task, the experimental group has a significant advantage over the control group in the second test score, and as the translation difficulty increases, the algorithm effect is more prominent. In addition, at the vocabulary and grammar level, the experimental group's accuracy rate far exceeds that of the traditional algorithm group. This study verifies the effectiveness of the compressed sensing algorithm for translating complex, long English sentences, especially in high-difficulty translation tasks.

KEYWORDS

English Translation; Complex Long Sentences; Compressed Perception; IoT Technology

CITE THIS PAPER

Ruichao Li. Translation of English Complex Long Sentences Based on Natural Language Information Processing and Artificial Intelligence Compression Perception Algorithm. Advances in Educational Technology and Psychology (2026). Vol. 10, No. 4, 44-53. DOI: http://dx.doi.org/10.23977/aetp.2026.100407.

REFERENCES

[1] Pan J, Wong T M, Wang H. Navigating learner data in translator and interpreter training: Insights from the Chinese/English Translation and Interpreting Learner Corpus (CETILC). Babel, 2022, 68(2):236-266.
[2] Zhang X. A Study of Cultural Context in Chinese-English Translation. Region - Educational Research and Reviews, 2021, 3(2):11-14.
[3] Najjar I, Kadhim K A, Al-Heeh S. Morphological shift of hyperbolic patterns in the Quran, with reference to English translation. FORUM, 2021, 19(2):148-163.
[4] Saha A, Lee Y W, Hwang Y S. Context-aware block-based motion estimation algorithm for multimedia internet of things (IoT) platform. Personal and Ubiquitous Computing, 2018, 22(1):1-10.
[5] Chi Z. Research on satellite remote sensing image fusion algorithm based on compression perception theory. Journal of Computational Methods in Sciences and Engineering, 2021, 21(2):341-356.
[6] Lin G. Water resources data compression perception reconstruction optimization algorithm based on internet of water. technical Bulletin, 2017, 55(7):62-68.
[7] Hassager H G, Wiinberg A, Dau T. Effects of hearing-aid dynamic range compression on spatial perception in a reverberant environment. The Journal of the Acoustical Society of America, 2017, 141(4):2556-2568.
[8] Jia Z. Cross-border e-commerce personalized filtering recommendation model based on particle-level compression awareness. Technical Bulletin, 2017, 55(7):136-143.
[9] Ballesteros-Pea S, Hoz V. Accuracy of Healthcare Providers’ Perception of Chest Compression Depth and Chest Recoil. Signa Vitae, 2020, 16(1):159-162.
[10] Wolff J G. Information Compression as a Unifying Principle in Human Learning, Perception, and Cognition. Complexity, 2019, 2019(2):1-38.
[11] Meng Z, Li J, Long H. Fault Diagnosis Method for Rolling Bearings Based on Compression Information Feature Extractions. China Mechanical Engineering, 2017, 28(7):806-812.
[12] Goncalves P, Moraes C, Porto M. Complexity-Aware TZS Algorithm for Mobile Video Encoders. Journal of Integrated Circuits and Systems, 2019, 14(3):1-9.
[13] Chung K L, Liang Y C, Wang C S. Effective Content-Aware Chroma Reconstruction Method for Screen Content Images. IEEE Transactions on Image Processing, 2019, 28(3):1108-1117.
[14] Ma Y, Gang Z, Shan L. RoFi: Rotation-Aware WiFi Channel Feedback. Internet of Things Journal, IEEE, 2017, 4(5):1684-1695.
[15] Comino M, Chica A. Error-aware construction and rendering of multi-scan panoramas from massive point clouds. Computer Vision and Image Understanding, 2017, 157(C):43-54.
[16] Fulton B. Suggested Readings in Korean Literature in English Translation. Translation Review, 2020, 108(1):104-110.
[17] Muravev Y. TEACHING LEGAL ENGLISH TRANSLATION BY THE CASE METHOD IN RUSSIAN-ENGLISH LANGUAGE PAIR. Humanities & Social Sciences Reviews, 2020, 8(4):961-971.
[18] Wen H. Intelligent English translation mobile platform and recognition system based on support vector machine. Journal of Intelligent and Fuzzy Systems, 2020, 38(153):1-12.

Sponsors, Associates, and Links

---

• Curriculum and Teaching Methodology
  
  
• Transactions on Comparative Education
  
  
• Applied & Educational Psychology
  
  
• Advances in Vocational and Technical Education
  
  
• Adult and Higher Education