Intelligent Translation System and Network Communication Optimization of Tourism English for International Tourists
Main Article Content
Abstract
This study presents an intelligent English translation system for international tourists, integrating deep learning, multimodal translation, and network communication optimization. The system addresses challenges in language recognition, cultural context handling, and low-latency transmission across heterogeneous networks. A hybrid communication framework combining offline caching, cloud collaboration, and adaptive multi-network switching ensures reliable operation under varying network conditions. Scenario-based deployment and personalized service strategies enhance usability in diverse tourism environments, while rigorous evaluation demonstrates improved translation accuracy, response speed, and user satisfaction. The approach is particularly relevant for mobile communication and electromagnetic network environments, where low-latency and robust protocol design are essential for effective real-time translation and international service interoperability. Experimental results confirm that the system supports both high-quality language services and efficient technical exchanges in global tourism and connected device networks.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
K. J. Giri, N. A. Lone, R. Bashir, Bhat, and JI, “English Kashmiri Machine Translation System related to Tourism Domain,” 2024 11th International Conference on Computing for Sustainable Global Development (INDIACom), pp. 1713-1717, 2024, doi: 10.23919/indiacom61295.2024.10498213.
S. Li and Y. Huang, “BAS-ALSTM: Analyzing the efficiency of artificial intelligence-based English translation system,” Journal of Ambient Intelligence and Humanized Computing, vol. 15, no. 1, pp. 765-777, 2024, doi: 10.1007/s12652-023-04735-1.
J. Cui, “Research on Tourism English Translation System Based on Fuzzy Clustering Algorithm,” Journal of Intelligence and Knowledge Engineering, vol. 1, no. 4, pp. 1-4, 2023, doi: 10.62517/jike.202304411.
P. Lu and F. Xu, “The quality optimization of English–Chinese machine translation based on deep neural networks,” Discover Artificial Intelligence, vol. 5, no. 1, pp. 88, 2025, doi: 10.1007/S44163-025-00319-4.
A. A. Avagyan, “Special Features of Tourism Text Translation from Russian into English,” Litera, no. 11, pp. 264-274, 2024, doi: 10.25136/2409-8698.2024.11.71526.
X. Song, “Intelligent English translation system based on evolutionary multi-objective optimization algorithm,” Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, 2021, doi: 10.3233/jifs-189469.
Q. Qin, “Research on Automatic Error Recognition of Machine English Translation Based on Multi-feature Fusion,” 2024 Global Conference on Communications and Information Technologies (GCCIT), pp. 1-4, 2024.
X. Yu, “Research on English Translation System Combining Statistical Machine Translation and Deep Learning,” Region - Educational Research and Reviews, vol. 7, no. 4, 2025, doi: 10.32629/RERR.V7I4.3864.
X. Zhou and C. Shi, “Optimization of automatic English translation system in cross-border trade based on deep learning,” Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 6, pp. 3748-3761, 2024, doi: 10.1177/14727978241297019.
Y. Ting, “Design of English Translation System Based on Deep Learning,” Journal of Physics: Conference Series, vol. 1802, no. 4, Art. no. 042053, 2021, doi: 10.1088/1742-6596/1802/4/042053.
L. Natraj and S. D. Shetty, “A Translation System That Converts English Text to American Sign Language Enhanced with Deep Learning Modules,” International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 8, no. 12, pp. 2278-3075, 2019, doi: 10.35940/ijitee.l3781.1081219.
M. I. Arrahman and M. G. Mayasari, “English Cultural Terminologies in Tourism of North Sumatera Brochures: Translation Studies,” Invention: Journal Research and Education Studies, pp. 1-7, 2023.
Y. L. Yeong, T. P. Tan, and S. K. Mohammad, “Using Dictionary and Lemmatizer to Improve Low Resource English-Malay Statistical Machine Translation System,” Procedia Computer Science, vol. 81, pp. 243-249, 2016, doi: 10.1016/j.procs.2016.04.056.
H. Sajjad, N. Durrani, F. Guzman, P. Nakov, A. Abdelali, S. Vogel, et al., “Egyptian Arabic to English Statistical Machine Translation System for NIST OpenMT’2015,” arXiv preprint arXiv:1606.05759, doi: 10.48550/arXiv.1606.05759.
S. I. Eludiora, A. O. Agbeyangi, and O. I. Fatusin, “Development of English to Yorùbá Machine Translation System for Yorùbá Verbs’Tone Changing,” International Journal of Computer Applications, vol. 129, no. 10, pp. 12-17, 2015, doi: 10.5120/ijca2015907007.