Self-coding neural network processing method and device, computer equipment and storage medium

Abstract

The invention discloses a self-encoding neural network processing method and device, computer equipment and a storage medium, and the method comprises the steps: converting a text sample into a sampleword vector, inputting the sample word vector into a convolutional neural network model, and carrying out the preliminary feature extraction of the sample word vector, and obtaining a preliminary hidden feature of the sample; inputting the preliminary implicit features of the sample into a plurality of self-coding neural networks, training the self-coding neural networks to obtain a plurality ofself-coding neural network models, and inputting the preliminary implicit features of the sample into the self-coding neural network models for feature extraction to obtain sample implicit features output by the self-coding neural network models; clustering the extracted feature samples with the hidden features of the samples to obtain a clustering result; determining whether to reconstruct the self-coding neural network according to the clustering result; and if determining that the self-coding neural network needs to be reconstructed, constructing a target self-coding neural networ accordingto the contour coefficient, and acquiring the self-coding neural network with the high clustering accuracy.

Description

technical field [0001] The present application relates to the field of computers, in particular to a self-encoding neural network processing method, device, computer equipment and storage medium. Background technique [0002] With the acceleration of the pace of modern life, in order to browse as much information as possible in a short period of time, more and more short texts appear on the Internet. These short texts vary in structure and content. [0003] In order to analyze and count these short texts, the usual practice is to use the bag-of-words model to extract text features, and then cluster the extracted features through a clustering algorithm. However, because the bag-of-words model needs to ignore the word order, grammar, syntax and other elements of the text, and split the text into individual words, this approach often leads to the loss of text feature information due to the lack of neural networks for feature extraction, which leads to text feature extraction. ...

AI Technical Summary

Problems solved by technology

However, because the bag-of-words model needs to ignore the word order, grammar, syntax and other elements of the text, and split the text into individual words, this approach often leads to the loss of text feature information due to the lack of neural networks for feature extraction, which leads to text feature extraction. The accuracy rate is reduced, which affects the clustering accuracy rate

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