Application of artificial intelligence in automated painting creation and human–machine collaborative innovation

This study investigates the application of artificial intelligence in automated painting creation and human–machine collaborative innovation. To improve the emotional expression and creative flexibility of AI-generated paintings, an Emotional Semantic Guided Generation Model (ESGGM) was proposed. The model integrates emotional semantic encoding, style transfer and fusion, and creative intention reasoning. In addition, a human–machine collaborative creation system was designed to support user input, bidirectional feedback and personalized model adaptation. Experimental results show that ESGGM achieved a comprehensive artistic value score of 0.82, outperforming the CNN style transfer model, GAN and NST. The proposed collaborative system achieved a score of 0.85, higher than the rule-based interaction model and the sketch-to-painting model. These results indicate that emotional semantic guidance and interactive collaboration can improve the quality and controllability of AI-assisted painting creation.