An Optimized Decision Tree Model for Agricultural Product Placement Using Hierarchical Clustering Algorithm

Big data technologies have gradually penetrated various industries, leading to the development of transparency in agricultural market data information. At the same time, consumer behavior has become increasingly complex, and previous extensive management methods are no longer effective in increasing sales. This has led to the urgent need for marketplaces to adjust their management strategies to effectively increase sales and seize more market share. This paper proposes a method to address this issue: by using a decision tree model for classification and prediction and employing a cross-selection of decision tree algorithm and clustering algorithm to predict agricultural product data in the market area. The results show that the model is relatively accurate and can help agricultural markets develop product sales plans. The model's accuracy in predicting market conditions is high, and it has guiding value for the placement of agricultural products in various markets.