Research on Image Optimization of Brand IP Story Generation Based on Natural Language Processing

This study focuses on optimizing the brand IP story generation and image shaping process with the help of cutting-edge natural language processing technology. This paper constructs a brand IP story generation model based on natural language processing and proposes an emotional semantic fusion generation algorithm (ESSGA). The algorithm deeply integrates emotional analysis and semantic understanding, can accurately capture the core value and emotional appeal of the brand, and mine semantic associations to generate logically coherent and infectious stories. In the experimental stage, a rich data set covering brand information in multiple fields was carefully constructed, and the experimental environment was built using Python programming language and TensorFlow deep learning framework. The classic traditional story generation algorithm was selected for comparison. After testing, the average semantic coherence score of the story generated by the ESSGA algorithm was as high as 85 points (out of 100), which was an average increase of 20 % over the comparison algorithm; the fit between the generated story and the brand image reached 80 %, which was 15 % higher than the comparison algorithm. This fully shows that the ESSGA algorithm significantly improves the quality of brand IP stories, optimizes the brand IP image, and opens up a new technical path for brand building.