Pu’er tea is a Yunnan geographical indication product, and its brand value ranks first in China. At present, qualitative and quantitative methods with low prediction accuracy are used to predict price. In this paper, based on the current situation and industry characteristics, a differential autoregressive integrated moving average model (ARIMA) is used to predict the short-term price. From the perspective of macro and micro, back-propagation neural network model (BP) was established to predict the long-term price based on the weight ranking of 16 factors affecting the price by technique for order preference by similarity to ideal solution method (TOPSIS). The future price is predicted and analyzed, and then based on the empirical results, suggestions are put forward for the industry in terms of reducing production capacity, increasing consumer demand and combining with the publicity and promotion of Internet.

普洱茶是云南地理标志产品，品牌价值居全国第一。目前，预测价格多采用预测准确率较低的定性和定量方法。本文根据现状和行业特点，采用差分自回归综合移动平均模型（ARIMA）对短期价格进行预测。从宏观和微观的角度，建立了反向传播神经网络模型（BP），通过与理想解相似度法（TOPSIS ）。对未来价格进行预测分析，然后根据实证结果，为行业提出去产能方面的建议，