Sensitive information query method based on deep learning

Abstract

The invention discloses a sensitive information query method based on deep learning, which comprises the following steps: step 1, carrying out word segmentation processing on a text to be queried, andthen converting the text to be queried into a feature vector; and 2, inputting the feature vector obtained in the step 1 into a neural network model, outputting the similarity with a sensitive word library, if the similarity is higher than a threshold value, judging that the to-be-queried text contains sensitive words, and outputting a corresponding sensitive word result. According to the sensitive information query method based on deep learning, on one hand, by setting the word segmentation rule and training and updating the word segmentation rule, accurate word segmentation processing can be flexibly carried out on the text, and the word segmentation accuracy is improved; and on the other hand, by introducing an artificial intelligence technology, adopting a deep learning method and constructing a neural network model, the text is accurately and effectively recognized, the query accuracy is improved, and the query efficiency is improved.

Description

technical field [0001] The invention belongs to the technical field of information query, and in particular relates to a sensitive information query method based on deep learning. Background technique [0002] When publishing information on public platforms, it is usually necessary to check sensitive words, and content that violates laws or public order and good customs will not be made public. At first, manual review was adopted, but as more and more people use the Internet to publish information, the speed of manual review is very slow, and it is easy to miss, and the effect is not good; with the development of natural language query technology, people began to use various query schemes Carry out automatic screening to check whether the information to be released contains the content in the text library, but this query method is relatively mechanical and prone to misoperation. For example, in the sentence "ABCDEFG", AB is a phrase, CD is a phrase, BC is a sensitive word, ...

AI Technical Summary

Problems solved by technology

At first, manual review was adopted, but as more and more people use the Internet to publish information, the speed of manual review is very slow, and it is easy to miss, and the effect is not good; with the development of natural language query technology, people began to use various query schemes Carry out automatic screening to check whether the information to be released contains the content in the text library, but this query method is relatively mechanical and prone to misoperation. For example, in the sentence "ABCDEFG", AB is a phrase, CD is a phrase, BC is a sensitive word, then the existing query method will determine that there is a sensitive word in the sentence, resulting in misjudgment

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