Scholarlink

<p>With the boom of e-commerce, the online sale of agricultural products has become a key link between farmers and consumers. However, the seasonality of agricultural products and the volatility of markets pose significant challenges to sales forecasting, which directly affects inventory management and sales strategies. Based on the Long Short-Term Memory (LSTM) model, this study creatively proposes a new method for forecasting agricultural product sales, and constructs a unique consumption recommendation system, which is committed to significantly improving the prediction accuracy and consumer experience. The LSTM model was deeply optimized and the results were amazing, with a significant reduction of 20% in sales forecast error and an increase in forecast accuracy of more than 90%, far exceeding that of traditional models. Moreover, the innovative integration of multi-factor analysis such as weather and holidays has made the prediction accuracy soar to 93%, fully demonstrating the strong adaptability of the model in complex environments. Based on this, the consumer recommendation system can provide personalized recommendations based on consumer preferences and predicted hot-selling products, and after the system is launched, the user conversion rate will increase by 10%, and the average order value will increase by 8%, which will effectively promote the sales of agricultural products.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Research on sales forecasting and consumption recommendation system of e-commerce agricultural products based on LSTM model

  • 期刊论文

  • 发表日期：

    2025年4月16日

查看全文
  • Congcui Jiang

摘要

With the boom of e-commerce, the online sale of agricultural products has become a key link between farmers and consumers. However, the seasonality of agricultural products and the volatility of markets pose significant challenges to sales forecasting, which directly affects inventory management and sales strategies. Based on the Long Short-Term Memory (LSTM) model, this study creatively proposes a new method for forecasting agricultural product sales, and constructs a unique consumption recommendation system, which is committed to significantly improving the prediction accuracy and consumer experience. The LSTM model was deeply optimized and the results were amazing, with a significant reduction of 20% in sales forecast error and an increase in forecast accuracy of more than 90%, far exceeding that of traditional models. Moreover, the innovative integration of multi-factor analysis such as weather and holidays has made the prediction accuracy soar to 93%, fully demonstrating the strong adaptability of the model in complex environments. Based on this, the consumer recommendation system can provide personalized recommendations based on consumer preferences and predicted hot-selling products, and after the system is launched, the user conversion rate will increase by 10%, and the average order value will increase by 8%, which will effectively promote the sales of agricultural products.