Research on the Application of Deep Neural Network and Human-Computer Interaction Technology in Art Design

This paper introduces interactive-aware multi-objective style transfer network, an innovative framework designed to enhance digital artistic workflows by balancing computational efficiency, creative autonomy, and ethical transparency. By integrating a dual-path network for content preservation and style evolution, meta-learning for rapid style adaptation, and a hybrid evaluation system, interactive-aware multi-objective style transfer network achieves 85.7% style retention across diverse domains while reducing convergence iterations by 19.2%. The framework also employs gradient-weighted class activation mapping to align artificial intelligence, decisions with designer intent, achieving 78% congruence. These advancements address key limitations in opacity, latency, and domain generalization, providing a robust solution for intelligent creative tools. This work is significant for academic researchers and information technology professionals focused on advanced data processing and human-centered design.