Abstract
The development of international agriculture trade during the COVID-19 pandemic has encountered significant challenges. The processing of international agricultural trade data using machine learning techniques needs to be improved to perform effective analysis of agricultural trade. An essential issue for international agricultural trade is the accurate yield estimation for the numerous crops involved in international trade. Data mining techniques are the necessary approach for accomplishing practical and effective solutions for this problem. This paper combined the bidirectional encoder representations from transformers (BERT) model to conduct data mining and developed a trade data analysis system with efficient data analysis capabilities. Our results indicate that our model does reasonably well and obtains adequate information in deciding international agricultural trade. It can also be instrumental for policy and decision-making regarding international agricultural trade.
Highlights
Introduction e COVID19 pandemic has completely disrupted the world economy. e world economy is facing a reshuffle situation; in particular, the agricultural economy is experiencing more significant fluctuations and challenges
Data mining provides options in terms of algorithms and parameters within the algorithms. erefore, this study focuses on developing international agricultural trade in combination with the data mining model to improve the effectiveness of data mining as much as possible and overcome the existing problems [6]. e major contributions of this work are as follows: (1) is work combines the bidirectional encoder representations from transformers (BERT) model to conduct international agricultural trade data mining and analyzes the current situation of China’s agricultural international trade
BERT stands for bidirectional encoder representations from transformers
Summary
E processing of international agricultural trade data using machine learning techniques needs to be improved to perform effective analysis of agricultural trade. To extract information from these huge data assets, we need to use advanced approaches like data mining algorithms, in addition to standard statistical processing [5]. Erefore, this study focuses on developing international agricultural trade in combination with the data mining model to improve the effectiveness of data mining as much as possible and overcome the existing problems [6]. (1) is work combines the BERT model to conduct international agricultural trade data mining and analyzes the current situation of China’s agricultural international trade (2) It analyzes the future situation of China’s agricultural international trade and explores the future development direction of international agricultural trade e remainder of the paper is structured as follows.
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