Research on export prediction model based on machine learning: application of China’s agricultural products export

Abstract

In international trade, the testing of agricultural products is an important link in ensuring trade safety and food quality. Due to the direct impact of agricultural products on human health and trust in international trade, strict testing and verification of them is necessary. Different countries and regions may have different testing standards and requirements for agricultural products, but overall they are committed to ensuring the quality, safety, and traceability of agricultural products. Under the new situation, the world is facing challenges such as the epidemic and food security. The outbreak of the epidemic has brought uncertainty to international trade and food supply chains, and countries have strengthened testing and prevention measures for the import and export of agricultural products. At the same time, with population growth and economic development, agricultural resources and environment are also facing pressure, such as issues in land use, water resource management, and pesticide use, which have put forward new requirements for the quality and sustainable development of agricultural products. As a major agricultural country, China exports a large amount of agricultural products every year. In order to meet the demand for agricultural products in the international market, China needs to ensure that its agricultural products meet international standards and ensure their quality and safety through strict testing processes. At the same time, China is also making efforts to strengthen the sustainability of domestic agricultural production and environmental protection, in order to protect agricultural resources and improve the quality of agricultural products. In the era of Big data, the demand for data analysis in various industries is increasing. Machine learning (ML), as an important data analysis tool, plays a crucial role in agricultural product prediction and decision support. This paper compares multiple machine learning methods such as KNN, SVM, RF, and ARIMA, explores their feasibility in predicting Chinese agricultural products, and selects the best method. The efficient acquisition of key information through machine learning can help decision-makers better understand market demand, optimize agricultural product production and supply chain management, thereby promoting the improvement of agricultural product quality and the development of the agricultural industry. In summary, strict testing of agricultural products plays a crucial role in international trade. Faced with the challenges of the new situation, machine learning has become an important tool for obtaining critical information. By studying the feasibility of different machine learning methods, prediction and decision support can be provided for decision-makers, further improving the quality of agricultural products and enhancing the development level of the agricultural industry.