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Constructing a perfect urban fresh agricultural product supply system is the basic guarantee for the stability of urban life, and it is also the basic condition for supporting urbanization. This paper combines unsupervised learning algorithms to predict and analyze agricultural product consumption behaviors, determines whether to generate new neurons through the cumulative error value of the winning neurons, and gives a network model that can dynamically self-grow. Moreover, this paper constructs an agricultural product consumption behavior prediction model based on unsupervised learning and uses data to verify the performance of the algorithm in this paper. After confirming the performance test of the algorithm, it verifies the prediction effect of this method on the consumption behavior of agricultural products. Through statistical analysis of data, it can be known that the prediction method of agricultural consumption behavior based on unsupervised learning has a certain effect.

Research on Forecasting Methods of Agricultural Products Consumption Behavior Based on Unsupervised Learning