人工智能在“一带一路”倡议研究中的应用

doi:10.6040/j.issn.1672-3961.0.2024.337

摘要/Abstract

摘要： 随着我国“一带一路”倡议的深入推进,人工智能在风险评估与预测、舆情情感分析与热点议题挖掘、交通物流与贸易优化、环境保护与可持续发展以及文化传播与教育等多个关键领域的应用日益广泛。人工智能通过深度学习、机器学习和自然语言处理等技术,显著提升风险预测能力和管理精度,优化物流网络效率,促进绿色发展,增强跨文化交流效果。本研究系统综述了近年来人工智能在“一带一路”各关键领域的研究进展,重点探讨这些技术在实际应用中的具体成效与创新点,分析当前研究存在的数据获取质量不高、人工智能模型可解释性不足和跨领域协同与应用转化困难等局限,并提出未来研究的发展方向。

关键词: 人工智能, “一带一路”, 风险评估, 可持续发展, 跨文化交流

Abstract: With the deepening implementation of the Belt and Road Initiative(BRI), artificial intelligence(AI)has been increasingly applied in several key fields, including risk assessment and prediction, public opinion sentiment analysis and hot topic mining, transportation logistics and trade optimization, environmental protection, and sustainable development, as well as cultural communication and education. Artificial intelligence, through technologies such as deep learning, machine learning, and natural language processing, has significantly improved risk prediction capabilities and management accuracy, optimized logistics network efficiency, facilitated green development, and enhanced cross-cultural communication. This study systematically reviewed the research progress of artificial intelligence in various key fields of the Belt and Road in recent years, focusing on specific achievements and innovations in practical applications. The limitations of current research, such as low data acquisition quality, insufficient interpretability of artificial intelligence models, and difficulties in cross-domain collaboration and application transformation were analyzed. The future research directions were proposed.

Key words: artificial intelligence, the Belt and Road, risk assessment, sustainable development, cross-cultural communication

中图分类号:

TP181

吴昊. 人工智能在“一带一路”倡议研究中的应用[J]. 山东大学学报 (工学版), 2025, 55(5): 88-100.

WU Hao. The application of artificial intelligence in the study of the Belt and Road Initiative[J]. Journal of Shandong University(Engineering Science), 2025, 55(5): 88-100.

参考文献

[1] 向鹏成,高天,段旭,等. 基于机器学习的“一带一路”投资国别风险预测研究[J]. 工业技术经济, 2024, 43(7): 150-160. XIANG Pengcheng, GAO Tian, DUAN Xu, et al. Investment country risk prediction of "the Belt and Road" based on machine learning[J]. Journal of Industrial Technological Economics, 2024, 43(7): 150-160.
[2] 杨柳. 基于深度学习的企业“一带一路”投资风险智能预警研究[D]. 武汉: 武汉理工大学, 2021. YANG Liu. Research on intelligent early warning of enterprise "One Belt One Road" investment risk based on deep learning[D]. Wuhan: Wuhan University of Technology, 2021.
[3] 石京民, 王万君, 李健. 基于深度学习的“一带一路”沿线省域普惠金融发展水平评价[J]. 经济问题探索, 2020, 41(12): 139-149. SHI Jingmin, WANG Wanjun, LI Jian. Evaluation of the inclusive financial development level of provinces along the Belt and Road based on deep learning[J]. Inquiry into Economic Issues, 2020, 41(12): 139-149.
[4] 郑明贵,王馨悦,顾东明, 等. “一带一路”背景下巴基斯坦矿业投资环境风险评价与预测[J]. 黄金科学技术, 2023, 31(4): 646-658. ZHENG Minggui, WANG Xinyue, GU Dongming, et al. Environmental risk assessment and prediction of mining investment in Pakistan under the background of the Belt and Road[J]. Gold Science and Technology, 2023, 31(4): 646-658.
[5] LIANG P, YU M, JIANG L. Energy investment risk assessment for nations along China's Belt & Road Initiative: a deep learning method[J]. Applied Sciences, 2021, 11(5): 2406.
[6] 陈翔. “一带一路”背景下中国海外利益涉恐风险评估模型构建研究[D]. 北京: 中国人民公安大学, 2023. CHEN Xiang. Research on the construction of China's overseas interests related to terrorism risk assessment model under the background of "Belt and Road Initiative"[D]. Beijing: People's Public Security University of China, 2023.
[7] 雷懿. 基于机器学习的跨境电商境外战略环境评价与战略选择——面向“一带一路”目标市场的研究[D]. 北京: 北京交通大学, 2023. LEI Yi. Machine learning-based research on the overseas strategic environmental and strategic choice of China's cross-border e-commerce, towards the target markets in "the Belt and Road" countries[D]. Beijing: Beijing Jiaotong University, 2023.
[8] LI Z, PU M, SONG J, et al. Application of deep belief network for sustainable development via deep learning to export credit risk assessment under the Belt and Road strategy[C] //Proceedings of Applied Mathematics, Modeling and Computer Simulation(AMMCS). Wuhan, China: IOS Press, 2023: 63-77.
[9] 赵秋盈. 基于文本挖掘的金砖国家“一带一路”新闻话语策略研究[D]. 西安: 西安外国语大学, 2023. ZHAO Qiuying. Text mining based discursive strategies of BRI news in BRICS[D]. Xi'an: Xi'an International Studies University, 2023.
[10] 姜杰. 社交媒体文本情感分析[D]. 南京: 南京理工大学, 2017. JIANG Jie. Sentiment analysis of social media texts[D]. Nanjing: Nanjing University of Science and Tech-nology, 2017.
[11] 陈龙,管子玉,何金红,等. 情感分类研究进展[J].计算机研究与发展, 2017, 54(6): 1150-1170. CHEN Long, GUAN Ziyu, HE Jinhong, et al. A survey on sentiment classification[J]. Journal of Computer Research and Development, 2017, 54(6): 1150-1170.
[12] PANG Bo, LEE Lillian, VAITHYANATHAN Shiva-kumar. Thumbs up? Sentiment classification using machine learning techniques[C] //Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing(EMNLP). Stroudsburg, USA: Association for Computational Linguistics, 2002: 79-86.
[13] YOON Kim. Convolutional neural networks for sentence classification[C] //Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing(EMNLP). Doha, Qatar: Association for Computational Linguistics, 2014: 1746-1751.
[14] JOHNSON Rie, ZHANG Tong. Effective use of word order for text categorization with convolutional neural networks[C] //Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies(NAACL HLT). Denver, USA: Curran Associates, 2015: 103-112.
[15] 王洁,乔艺璇,彭岩,等. 基于深度学习的美国媒体“一带一路”舆情的情感分析[J]. 电子技术应用, 2018, 44(11): 102-106. WANG Jie, QIAO Yixuan, PENG Yan, et al. Sentiment analysis about "One Belt, One Road" public opinion of American media based on deep learning[J]. Application of Electronic Technique, 2018, 44(11): 102-106.
[16] CHANDRA J K, CAMBRIA E, NANETTI A. One Belt, One Road, one sentiment? A hybrid approach to gauging public opinions on the new silk road initiative[C] //Proceedings of 2020 International Conference on Data Mining Workshops(ICDMW). Sorrento, Italy: IEEE, 2020: 7-14.
[17] 祁瑞华, 付豪. “一带一路”智库报告主题挖掘与演化研究[J]. 智库理论与实践, 2022, 7(5): 11-19. QI Ruihua, FU Hao. Topic mining and evolution of “Belt and Road Initiative” report in think tanks[J]. Think Tank: Theory & Practice, 2022, 7(5): 11-19.
[18] 王伟豪. 欧洲智库视角下的“一带一路”倡议——基于文本分析的方法[D]. 北京: 北京外国语大学, 2023. WANG Weihao. The Belt and Road Initiative from the perspective of European think tanks: a textural analysis-based approach[D]. Beijing: Beijing Foreign Studies University, 2023.
[19] WANG Q, QIN K, YU Y, et al. Analysis on the Belt and Road Initiative flight network based on Markov clustering and complex network[C] //Proceedings of IEEE 1st International Conference on Civil Aviation Safety and Information Technology(ICCASIT). Kunming, China: IEEE, 2019: 635-639.
[20] XU Q, SHEN L, JIANG Y, et al. Multimodal transport routing problem considering transshipment and accessibility: the case of the "One Belt One Road" Initiative[C] //Proceedings of the 4th International Conference on Transportation Information and Safety(ICTIS). Ahmedabad, India: IEEE, 2017: 936-942.
[21] LI Qin, WANG Yu, XIONG Yu, et al. Machine learning-based optimization in a two-echelon logistics network for the dry port operation in China[J]. International Journal of Systems Science: Operations & Logistics, 2023, 10(1): 2252321.
[22] ZHANG L, WANG Z. Evaluation and analysis of cross-border logistics security risk between China and Kazakhstan based on SVM under the background of "One Belt and One Road"[C] // Proceedings of Advances in Artificial Systems for Medicine and Education V. Berlin, Germany: Springer, 2022: 196-205.
[23] TSYMBAL L, NATSVLISHVILI T, VERDENHOFA O. One Belt, One Road project: the impact of smart technologies on infrastructure and logistics[J]. Baltic Journal of Economic Studies, 2023, 9(3): 214-221.
[24] ZHOU H, XIAO F, LIU H, et al. Research on the OBOR trade network based on the maximal weighted spanning tree and centrality analysis strategy[C] //Proceedings of the 34th Chinese Control and Decision Conference(CCDC). Hefei, China: IEEE, 2022: 5853-5859.
[25] ZHOU H, ZOU H, LIU J, et al. Research on classification of countries along One Belt and One Road: clustering v.s. domain expert method: based on points table of essential data about the level of cooperation[C] //Proceedings of 2020 Chinese Control and Decision Conference(CCDC). Hefei, China: IEEE, 2020: 3560-3567.
[26] LEI Yi, QIU Xiaodong. Research on the evaluation of cross-border e-commerce overseas strategic climate based on decision tree and adaptive boosting classification models[J]. Frontiers in Psychology, 2021, 12(1): 803989.
[27] AAMIR M, BHATTI M A, BAZAI S U, et al. Predicting the environmental change of carbon emission patterns in South Asia: a deep learning approach using BiLSTM[J]. Atmosphere, 2022, 13(12): 2011.
[28] LI Jing, ZHOU Yanping, CHEN Huiying. Measure-ment, influencing factors and prediction on carbon emission performance of countries along the Belt and Road[J]. Clean Technologies and Environmental Policy, 2024, 26(3): 821-838.
[29] 纪玉俊, 高自金. “一带一路”倡议对绿色产品贸易的影响——基于双重机器学习方法的分析[J]. 调研世界, 2024, 11: 57-69. JI Yujun, GAO Zijin. The impact of Belt and Road Initiative on green product trade—based on the double machine learning approach[J]. The World of Survey and Research, 2024, 11: 57-69.
[30] LI Q. Virtual reality Chinese teaching system based on deep learning algorithm[C] //Proceedings of 2020 International Conference on Data Processing Techniques and Applications for Cyber-Physical Systems: DPTA 2020. Singapore: Springer, 2021: 1437-1442.
[31] WANG N, LI F. Research on the openness of regions along the Belt and Road based on machine learning[C] //Proceedings of the 3rd International Conference on Business Administration and Data Science(BADS 2023). Kashi, China: Atlantis Press, 2023: 166-172.
[32] ZHENG S. Research on GM-LSTM hybrid model for tourism prediction based on One Belt and One Road[D]. Toyama: University of Toyama, 2020.
[33] JIN D. Construction of "One Belt and One Road" intelligent analysis system based on cloud model data mining algorithm[C] //Proceedings of the 11th International Conference on Intelligent Computation Technology and Automation(ICICTA). Shenzhen, China: IEEE, 2018: 282-285.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed