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Issue title: Fuzzy System for Economy Back on Track
Guest editors: Anand Paul, Simon K.S. Cheung, Chiung Ching Ho and Sadia Din
Article type: Research Article
Authors: Chen, Zijuana; * | Lian, Yinga | Lin, Zhipengb
Affiliations: [a] Department of Art and Media, Fujian Forestry Vocational Technical College, Nanping, Fujian, China | [b] Department of Automation Engineering, Fujian Forestry Vocational Technical College, Nanping, Fujian, China
Correspondence: [*] Corresponding author. Zijuan Chen, Fujian Forestry Vocational Technical College, China. E-mail: chenzijuan19880116@163.com.
Abstract: Due to various factors, the learning process of business English is mostly autonomous learning. However, the traditional autonomous learning model is difficult to effectively improve the learning effect of business English. In order to improve the business English learning model, based on artificial intelligence and improved BP network model, this paper builds a business intelligence autonomous learning system with certain intelligence. Moreover, this paper designs functional modules for the characteristics of business English learners, and combines the self-learning needs to facilitate the processing of structural functions, so that students can complete the operation independently. The system sets up multiple functional modules, conducts guided recommendation learning according to the characteristics of the self-learning process, and combines the feedback system to correct the shortcomings in students’ autonomous learning. Through this system, teachers can perform a variety of operations offline and eliminate restrictions on location and teaching time. In addition, in order to verify the performance of the model, the experimental study was conducted by setting up a control group and an experimental group. The research results show that the model constructed in this paper has good performance.
Keywords: Artificial intelligence, improved algorithm, BP neural network, business English
DOI: 10.3233/JIFS-189544
Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 7159-7170, 2021
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