Abstract: The price of agricultural products has always been the key area for maintaining social and economic life.Due to the non-linear relationship between predicted prices and influencing factors of agricultural products,recurrent neural networks are suitable for time series prediction.However,for long-term span,its prediction effect is limited.According to the price characteristics of agricultural products,a neural network model of LSTM-DA (Long Short-Term Memory-Double Attention) was designed.It combines the convolutional attention network,the Long Short-Term Memory network and the attention mechanism.The attention factors of different components are extracted by the convolutional attention network,and the corresponding weights are adjusted and fed into the Long Short-Term Memory network mo-del.Based on the influence of the time series,the results are sent to the attention mechanism for weight adjustment,and finally the results are used for short-term prediction of agricultural product price index.Before the experiment,the multi-threading mechanism is used to crawl a large number of agricultural information platforms to collect a large amount of price,weather and other related data.Based on the analysis and cleaning,they are stored in a Hadoop Distri-buted File System.In the experiment,the Long Short-Term Memory network is used as the baseline.Compared with the traditional single model,this model can improve the prediction accuracy,and the predicted price index can accurately describe the overall trend of vegetable products in the next week.

Key words: Attention mechanism, Convolutional attention network, Long short-term memory, Network data crawler, Price forecasting