Research on Intelligent Semantic Checking Algorithm of Article Editing Based on Artificial Intelligence

In this paper, a semantic checking algorithm based on long-short-term memory neural network is proposed to solve the problem of word errors in article editing. To a certain extent, the algorithm solves the problem that the wrong words cannot be intelligently checked and prompted in the article editing work. The model trained according to the corpus of different topics can solve the problem of difficult word error correction in the editing of related articles. After analyzing the requirements of semantic inspection algorithms, this paper first designs and implements two word embedding algorithms in data processing, and then trains a language model based on long and short-term memory networks. Finally, after analyzing the test results of the model, the optimal semantic check effect. And realized the semantic checking algorithm based on neural network machine learning. This algorithm first collects, organizes and constructs a standard corpus data set; secondly, the word embedding algorithm is used to map the processed corpus data to the word vector space; finally, it is trained to identify sentence segments and analyze the sentence segments according to the results obtained by the word embedding algorithm.