Abstract
With the rapid development of agricultural product sales data, the traditional prediction model cannot meet the processing needs. Based on deep learning theory, an improved ICM agricultural product sales prediction model using the softmax classifier is proposed. Introducing the sparse autoencoder in ICM can reduce feature loss. The features also can be extracted. In addition, using the pretreatment mode based on fuzzy membership theory, we can obtain the fuzzy correspondence of considerations and grades of agricultural product sales. At the same time, the precision of prediction for the model is further optimized. It can be seen that the agricultural product sales prediction model based on improved ICM can realize the real-time prediction of agricultural product sales. The maximum classification accuracy of the model can reach 80.98%, which means that it has certain practical application value.
Highlights
To ensure general applicability of the improved ICM model, 3000 samples are randomly selected from the experimental dataset to be the feature learning subset, and 1000 samples are selected as classification learning subset, including 600 softmax classifier training sets and 400 test sets
Precision and recall are used to evaluate model classification performance in this paper, and false positive rate (FPR) and false negative rate (FNR) are used to evaluate risk. e specific calculation methods are shown in formulas (25)–(28)
The proposed prediction method of agricultural product sales based on deep learning uses sparse autocoding to reduce the loss of data feature as much as possible
Summary
Agricultural products are necessities of daily life. With the increase of sales platform, the agricultural product sales model is more diversified, and the online sales model has gradually become the mainstream sales model of agricultural products. E current online precision sales of agricultural products are mainly through the shallow-layer deep learning network model. It extracts the value information from the massive agricultural product sales data to predict the trend of sales changes, so as to help merchants adjust the sales strategy in real time. Chen et al and Eric et al implemented online sales prediction of agricultural products by using multitask recursive neural network based on trend alignment [7, 8]. The prediction performance of the shallow-layer model is limited, so the real-time analysis processing of agricultural product trading data cannot be realized. At the same time, considering that agricultural sales data are growing exponentially every day, the semisupervised learning model is the main means of predicting such rapid growth data. It puts forward a superimperial crown model based on the ICM to predict the agricultural product sales
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