Abstract
In this paper, firstly, the ARIMA time-series differential autoregression algorithm was used to establish the change model of the number of bee colonies, and then the entropy weight method TOPSIS and Pearson correlation matrix were fused to analyze the index sensitivity. Finally, the prediction model of the number of bee colonies was established based on BP dynamic neural network, to complete the prediction and index analysis of bee colonies. Finally, we will discuss the findings from mathematical modeling of the blog summary writing, which show that the colony's optimal temperature, climate, feed supply, and other environmental conditions have a significant impact on the rate of soft production.
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