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A Classification Method of Tourism English Talents Based on Feature Mining and Information Fusion Technology

机译:基于特色矿业和信息融合技术的旅游英语人才分类方法

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With the rapid development of the Internet, text data has become one of the major formats of big data tourism and improves the quality and promotes the optimization and upgradation of tourism English talents. This paper proposes a model of tourism English talent resources based on data mining techniques using a big data framework. The characteristic distribution structure model is built to identify and blend the characteristics of tourism English talent resources. Connection feature mining and information fusion are combined to share data and schedule resources during the talent training process. Initially, the proposed research work uses a cloud storage system for developing intercultural communicative competence of tourism English talents. Next, the optimal scheduling design of tourism English talent training resource’s big data is carried out. Finally, the fuzzy clustering method deals with the adaptive clustering of tourism English talent resource distribution big data. The simulation findings show that the proposed method has high precision and big data computation efficiency. Moreover, it can successfully mentor the development of a new framework of tourism English talent training.
机译:随着互联网的快速发展,文本数据已成为大数据旅游的主要格式之一,提高质量,促进旅游英语人才的优化和升级。本文提出了一种基于使用大数据框架的数据挖掘技术的旅游英语人才资源模型。建立了特征分布结构模型,以识别和混合旅游英语人才资源的特征。连接特征挖掘和信息融合组合以在人才培训过程中共享数据和计划资源。最初,拟议的研究工作采用云存储系统来发展旅游英语人才的跨文化交际能力。接下来,进行旅​​游英语人才培训资源大数据的最佳调度设计。最后,模糊聚类方法涉及旅游英语人才资源分布大数据的自适应聚类。仿真结果表明,该方法具有高精度和大数据计算效率。此外,它可以成功推导推导出发展的旅游英语人才培训框架。

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