Game Character Art Design and Emotional Resonance Optimization Model Based on Deep Learning

This article takes deep learning (DL) technology as the core, and discusses its application in game character art design and emotional resonance optimization. By constructing a feature extraction model based on Convolutional Neural Network (CNN), we can accurately capture and understand the characteristics of game characters. At the same time, the natural language processing (NLP) technology is used to deeply explore players’ preferences and emotional needs. Combining the results of CNN and NLP, it provides a scientific and objective role design assistance and optimization scheme for designers. The research results show that the model has obvious advantages in character feature extraction, player preference analysis and design assistance and optimization. The optimized game characters have significantly improved their emotional resonance scores, and also improved the players’ satisfaction and willingness to choose to a certain extent, thus effectively enhancing the appeal of the game. The optimization model of game character art design and emotional resonance based on deep learning proposed in this study provides a new idea for the field of game character design.