A Study on Enhancing the Efficiency of Digital Content Generation and Distribution Using Natural Language Processing Techniques

Under the background of the digital era, the self-media platform breaks the information barriers between the communicators and the receivers, effectively alleviating the information asymmetry problem between the two.Through observation and research, this paper finds that the current channels for receivers to obtain digital information can be divided into user-generated content (UGC), professional-generated content (PGC), and brand-generated content (BGC) according to the classification of the main body, but most of the managers are negligent in the management of these digital contents, and do not really utilize the value of their dissemination.Digital content generation and dissemination based on natural language processing (NLP) technology has become an important way to solve this problem.The method is based on the unified processing of a large amount of corpus, input Word2vec model and Skip-gram model two types of language models for training, with the obtained language model for the required text can be obtained word vectors, the different lengths of the text will be unified vectorization.By introducing evaluation indexes such as dissemination efficiency, content quality and coverage, the effect of generated content can be measured objectively.The value of generating digital content to improve the dissemination efficiency is verified through the evaluation of the actual effect.