Automatically calculate the smoothness of the text (Language confusion), discard the articles with low fluency, and leave the articles with high smoothness to users.
Automatically calculate the correlation between title and description and keywords. If the correlation is low, keywords can be automatically inserted into the title and description to improve the relevance. You can also set a prefix keyword for the title, and randomly select one of the prefixes to add to the title of the article.
Text identification based on machine learning algorithm can audit the collected content and ensure the security of user content.
To achieve the pseudo original function based on synonym replacement, we can select the most suitable words from the 20 million pairs of synonym database to replace the words in the original text, so as to ensure the readability of the article.
To achieve the intelligent AI pseudo original based on machine learning, the original text is encoded into high-dimensional semantic vector, and then decoded word by word by decoder to realize the complete rewriting of the whole article. The pseudo original degree is high and the readability is good.
Automatic extraction of tags tags, and on this basis to achieve automatic internal chain, when the text corresponding to the tag appears in the text, add a link to the text to point to an article with the same topic, so as to realize the automatic scientific and effective inner chain construction.
You can also set fixed links. When some fixed text appears in the text, add fixed links to it, pointing to articles inside or outside the station.
