Semantic Analysis of Public Health Medical Issues Based on Convolution Neural Networks

Abstract

Text mining and semantic analysis of medical public health issues are the main points for intelligent medical interaction, but less relevant research has been done on them. This article conceives a convolutional neural network for the semantic classification of public health medical issues. The dual convolution layer is used to further reduce the dimension of the data, extract more in-depth information from the data, and map the features. Each convolution layer includes several convolution nuclei to extract semantic characteristics, and then, the complete connection layer is input to the classifier to obtain the results of the classification. To check the classification effect, the dictionary artificial construction and the double hidden-layers neuronal network are used for semantic classification, and the three methods are compared and tested on the six real datasets. The experimental results show that when the quality of the dataset is high, the convolution neural network method proposed in this paper exceeds the last two methods. The proposed method is higher than the construction of the artificial dictionary and the double hidden-layers neural network in the recall rate: 0.153 and 0.037, and greater than 0.07 and 0.01 for the F1 measure rate, respectively. When the quality of the dataset is general, the models of the three methods do not give good classification results. Finally, it is concluded that the convolutional neural network method conceived has a good semantic recognition performance in public health medical issues.