Abstract
Logistics information will have a certain impact on the price of agricultural products. Therefore, the price of agricultural products can be analyzed and predicted based on the logistics information. Firstly, based on logistics information, a basic model of agricultural product price is constructed. Then, support vector machine prediction algorithm and ensemble learning prediction method are applied to analyze the price relationship of agricultural products and emotional characteristics are added. Secondly, the price data of all kinds of agricultural products in various provinces of China are collected, and the algorithms constructed in this paper are used to test the price data. The results show that the accuracy of each model is improved after adding emotional feature index, which indicates that emotional features can better supplement the shortcomings of digital features, thereby improving the accuracy of prediction.
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Chen, L., Li, Z. Research on the price analysis and prediction method of agricultural products based on logistics information. Cluster Comput 22 (Suppl 6), 14951–14957 (2019). https://doi.org/10.1007/s10586-018-2462-y
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DOI: https://doi.org/10.1007/s10586-018-2462-y