Prediction Model of Yellow Rice Wine Raw Material Index Content Based on Depth Feature Selection Technology

In this paper, the depth feature selection technology is proposed. According to the different characteristics of feature extraction methods, a multi-layer feature extraction structure is formed, and selection variables are introduced to construct a global optimization model that enhances the feature expression of data sets from horizontal to vertical, so as to realize the adaptive feature selection of the prediction model as a whole. The real-coded harmony search algorithm was combined with BP, RNN and RBF neural network to optimize model structure and parameter. Experiments show that compared with the traditional prediction model, this method improves the prediction accuracy of each index value of raw materials corresponding to yellow rice wine products. The model determination coefficient is increased by 6.89%, and the mean square error is reduced by 7.43%. Food processing enterprises can select raw materials according to the predicted raw material index value.