AI Painting Effect Evaluation of Artistic Improvement with Cross-Entropy and Attention

With the rapid development of AI technology, AI painting tools are increasingly used in art creation. However, the effects of works created by different users using AI painting tools vary. Finding out the factors that affect the level of art creation after users use AI painting tools is a matter of concern. To solve this problem, this paper proposes a new Multi-Classification Attention Support Vector Machine (MCASVM) with cross-entropy loss function. By identifying and predicting the level of creativity of ordinary users after using AI painting tools, this model compares and analyzes the influencing factors behind the high and low effects of artistic creativity enhancement after using AI painting tools. The main contribution of this paper is to establish the Art Creation Ability Assessment Dataset (ACAAD) through real data collection to provide data support for subsequent assessments. Meanwhile, MCASVM directly handles the multi-classification problem in the established dataset by introducing multiple SVMs. Among other things, the probabilistic calibration network adjusts the model output so that its predicted probabilities are closer to the probability that the sample truly belongs to the classification. DBAM enhances the feature fusion capability of the model by explicitly focusing on the important channel and spatial features, and it enables the model to more accurately recognize and differentiate between changes in the creative abilities of different users before and after using AI painting tools. The experimental results show that the artistic creativity of ordinary users can be enhanced by AI painting tools, where the most central influencing factors are interest level and social support.