Agricultural Product Sales Prediction of ICM Neural Network Improvement by Sparse Autoencoder

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.