An information processing method and electronic device

Abstract

The invention discloses an information processing method, which is applied to an electronic device, and the electronic device can obtain a junk information sample database. When the electronic device receives a message, the method includes: passing a first predetermined rule judging whether the information is spam; when judging that the information is not spam according to the first predetermined rule, matching the information with the spam samples in the spam sample database, and judging whether the matching result satisfies The second predetermined rule: when the matching is unsuccessful, judge whether the information is spam according to the third predetermined rule. Through this method, the technical problem of limited spam filtering accuracy in the prior art is eliminated, and the technical effect of improving the spam filtering accuracy is achieved.

Description

technical field [0001] The invention relates to the field of electronic technology, in particular to an information processing method and electronic equipment. Background technique [0002] Spam filtering is the application of natural language processing technology to filter text messages containing deception, pornography, advertisements, etc. received by mobile phones and other instant messaging software. [0003] From the point of view of whether the content of information is legal, spam can be divided into illegal spam and common spam. The types of illegal spam information include fraud, pornographic services, invoices, etc. In order to avoid being filtered by the keyword filtering system, this type of spam information usually adopts variants for key words appearing in the information. For example, the variant of "invoice" is "fa\ ticket\". Ordinary spam mainly includes real estate advertisements, operator push information, business promotion information, etc. This type...

AI Technical Summary

Problems solved by technology

[0005] It is difficult to avoid frequent combinations of natural language by filtering keywords that appear in spam; to filter text based on the lexical and grammatical features of the text, it is difficult to filter out spam that does not contain obvious grammatical features; the scale of required data resources is large, It is difficult to deploy directly to the mobile client, and the information needs to be uploaded to the server for filtering, which has the risk of leaking user privacy

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