Enterprise classification method and system based on big data deep learning and electronic equipment

A deep learning and enterprise classification technology, applied in neural learning methods, text database clustering/classification, text database query, etc., can solve the problems of self-learning iteration, low classification efficiency, and inability to achieve accurate classification in cases where classification cannot be achieved. High learning ability and accuracy, reduce manual intervention, improve the effect of recognition and completion ability

Active Publication Date: 2021-04-09
广州友圈科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to overcome the deficiencies in the above-mentioned prior art, and provide a method, system and electronic equipment for enterprise classification based on deep learning of big data, which solves the problems in the prior art for enterprises and indu

Method used

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  • Enterprise classification method and system based on big data deep learning and electronic equipment
  • Enterprise classification method and system based on big data deep learning and electronic equipment
  • Enterprise classification method and system based on big data deep learning and electronic equipment

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0042] refer to figure 1 , the present embodiment provides a method for classifying enterprises based on big data deep learning, comprising the following steps:

[0043] S1: Obtain the comprehensive information of the enterprise and form a large data set;

[0044] S2: Based on the CRF word segmentation model and the probability graph model, extract the enterprise component keyword set, perform preprocessing actions, train the corresponding word vector model, and use the density clustering algorithm to predict several characteristic keywords for the constructed word vector model set, and remove noise words or update noise thesaurus;

[0045] S3: Use the FastText text classification model to perform TF-IDF screening on the word set, and use the LDA model to conduct topic analysis on large data sets, extract keywords about the company, and use the density clustering algorithm to predict a number of words based on expert threshold recommendations. a set of subject terms;

[004...

Embodiment 2

[0066] refer to figure 2 and image 3 , this embodiment 2 provides an enterprise classification system applied to the enterprise classification method based on big data deep learning in embodiment 1, including:

[0067] A corpus text module, the corpus text module is configured to obtain comprehensive information of enterprises to form a large data set; wherein, the corpus text module obtains enterprise Synthesize information, and sample and organize corpus texts from the big data set after big data cleaning, and this large amount of corpus texts constitutes a big data set;

[0068] A feature keyword generation module, the feature keyword generation module is configured to extract the enterprise component keyword set based on the CRF word segmentation model and the probability graph model, perform preprocessing actions, train the corresponding word vector model, and aim at the word vector model constructed , use the density clustering algorithm to predict several feature ke...

Embodiment 3

[0074] Embodiment 3 provides an electronic device, including a processor and a memory, at least one instruction, at least one program, code set or instruction set are stored in the memory, and the at least one instruction, at least one program, code set or instruction The set is loaded and executed by the processor to implement the enterprise classification method based on deep learning of big data in Embodiment 1.

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Abstract

The invention provides an enterprise classification method and system based on big data deep learning and electronic equipment, and the method comprises the steps: obtaining the comprehensive information of an enterprise, and forming a big data set; based on a CRF word segmentation model and a probability graph model, extracting an enterprise component keyword set, training a corresponding word vector model, and predicting and dividing a plurality of feature keyword sets by using a density clustering algorithm; carrying out TFI-DF screening on the word sets by utilizing a FastText text classification model, carrying out topic analysis on the big data set by utilizing an LDA model, extracting subject terms related to enterprises, and constructing a plurality of subject term sets by utilizing a density clustering algorithm; combining the feature keyword set and the subject term set to obtain a plurality of training samples, inputting the training samples into a bidirectional cycle neural network for training, and constructing a multi-category classification model; and carrying out classification prediction on enterprises by utilizing the multi-category classification model, matching a perfect threshold value, and automatically labeling industry labels of multiple hierarchies. The method has the characteristics of strong scene adaptability, high classification accuracy, high efficiency and reduced labor cost.

Description

technical field [0001] The invention belongs to the technical field of classification methods, and in particular relates to an enterprise classification method, system and electronic equipment based on big data deep learning. Background technique [0002] In the "2017 National Economic Industry Classification Notes" published by the National Bureau of Statistics on May 22, 2019, there are 20 first-level industry classifications and 97 second-level industry classifications. Mined third and fourth-level industry classification. Industry classification is particularly important for economic activity classification, information processing, and information exchange in national macro-management such as statistics, planning, finance, taxation, and industry and commerce. As the world's second largest economy, with the impact of industrial transformation, upgrading and the rise of new industries, more enterprises will continue to be incubated at a high speed, and comprehensive devel...

Claims

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Application Information

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IPC IPC(8): G06F40/284G06K9/62G06N3/08G06F16/33G06F16/35G06F16/36G06F40/242
CPCG06F40/284G06F40/242G06F16/35G06F16/3335G06F16/374G06N3/08G06F18/24
Inventor 罗根基
Owner 广州友圈科技有限公司
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