Auxiliary system of media content creation based on natural language processing and reinforcement learning

Against the background of accelerating global informatization, the media industry is undergoing profound changes. With the popularity of the Internet and social media, a considerable amount of information flocks to the public’s field of vision, which puts forward higher requirements for the timeliness, personalization, and creativity of media content. However, the traditional manpower-intensive content creation methods have made it challenging to meet the fast-paced needs of modern society. The fast-paced life of modern times has prompted people to pursue rapid access to and digestion of information. Traditional human-intensive content creation, such as writing, editing, and reviewing, can no longer keep up with this immediate demand. At the same time, audience segmentation requires media content to be accurately positioned, provide personalized services, and respond quickly to market changes. In today’s information explosion, maintaining high-quality content has become the core competitiveness of media, but it is difficult to balance timeliness and accuracy and depth of content in a manpower-intensive way. Therefore, in order to solve the existing problems, this study develops an intelligent content creation auxiliary system through natural language processing and reinforcement learning technology. Firstly, an NLP module is established to parse and generate high-quality text that conforms to grammatical rules and logical structure. Subsequently, the reinforcement learning mechanism is introduced to enable the system to have self-learning ability, and the strategy is constantly adjusted in multiple attempts to maximize the relevance and attractiveness indicators of the content. The system is applied to the natural environment of a prominent news website. Experiments show that within 3 months after the implementation of the system, compared with traditional hand-made manuscripts, the average click-through rate of the content created or recommended by it has increased by about 30%, and the user retention time has been extended by nearly 25%.