Analysis of 3D Virtual Art Design Model Based on GR-RFR Algorithm

Based on the limitations of traditional 3D (Three-Dimensional) virtual art design models in the creation process, such as lack of flexibility and efficiency, this paper introduces a design framework based on the GR-RFR (Generalized Regression-Random Forest Regression) algorithm to achieve more accurate and diverse generation of virtual artworks. Specifically, the GR-RFR algorithm is first used to establish a regression model for virtual artworks, fully considering the relationship between design parameters and visual effects. Then, through learning and fitting historical design data, key influencing factors are extracted, which will be used to optimize design decisions. Finally, based on the optimized model, the system generates and evaluates design solutions and feedback design results so as to quickly adjust and meet the personalized needs of artistic creation. The whole process relies on the adaptive characteristics of the GR-RFR algorithm, which effectively reduces human intervention and improves design efficiency. The GR-RFR algorithm shows high prediction accuracy in 3D virtual art design. The real and predicted art effect scores are mostly close, ranging from 7.2-9.3 points and 7.0-9.0 points, respectively. Further analysis shows that the absolute value of the error is mostly concentrated in 0.2-0.5 points, indicating that the model is stable and can better capture the relationship between design parameters and artistic effects. The 3D virtual art design model based on the GR-RFR algorithm effectively solves the flexibility and efficiency problems in traditional design methods, and provides a new direction to optimize the virtual art design process. Experimental results show that the algorithm can improve the automation and personalization level of design creation and has high practical value.