Research on Semantic Classification Method of Similar Labels Based On Deep Learning

  • Peng Bi, Feijuan He, Xianglin Miao, Liang Min


In recent years, with the advent of the era of mobile Internet, the amount of information and data in the network is growing exponentially, especially semi-structured and unstructured text data. This paper introduces the concept model of natural language to solve the high dimensional sparse representation and semantic gap in the traditional vector space model. For the training of natural language conceptual model, this paper adopts deep learning method to select training parameters. At the same time, in terms of keyword feature extraction, a method based on huffman tree is proposed to solve the feature extraction model of natural language text and optimize the initial weight between words and the influence transfer method of vocabulary nodes. By comparing with other research methods, the experimental results show that the proposed method is practical and effective in the field of text multi label classification.