Feasibility Evaluation Model for Art Entrepreneurship Projects Integrating Transformer
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Abstract
The feasibility assessment of art entrepreneurship projects requires effective integration of multi-source heterogeneous data, while traditional methods rely heavily on subjective judgment and simplified indicators, leading to biased evaluation results. This paper proposes a data-driven multimodal evaluation model integrating Transformer architectures. The model incorporates project business plans, artwork images, market performance data such as funding amounts and page views, and social media sentiment. A text Transformer extracts innovation and market potential features from business-plan text, a visual Transformer encodes style and visual appeal from artwork images, and a time-series network models dynamic funding and traffic trends. A multimodal Transformer is then used to fuse the four types of features and output a comprehensive feasibility score. Experimental results show that art NFT projects evaluated by the model achieve high scores in innovation, market potential, artistic value, and financial feasibility, with a financial feasibility score of 0.91 and a public attention score of 0.85. By contrast, the public art project receives lower scores across all indicators, including a financial feasibility score of 0.47. The results indicate that the model can effectively integrate heterogeneous project information and provide quantitative decision support for art entrepreneurship feasibility evaluation.
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