The analysis of entrepreneurship evaluation system for talent cultivation in artistic creativity and animation under artificial intelligence

This work aims to explore the application of artistic creativity using deep learning (DL) and artificial intelligence (AI) in the evaluation system of innovation and entrepreneurship. Moreover, this work intends to propose a new perspective for talent cultivation in animation majors in higher education institutions. Addressing the strong subjectivity and lack of quantitative indicators in traditional artistic creativity evaluation methods, this work constructs an artistic creativity innovation evaluation model based on the Backpropagation Neural Network (BPNN) integrated with the Style-Based Generative Adversarial Network (StyleGAN) algorithm. After preprocessing the artistic creativity image data, the data are input into the BPNN and StyleGAN fusion model. The model training uses the Adam optimizer, with 120 iterations set, and incorporates Dropout layers during training to enhance the model's generalization ability. The performance of the model is evaluated, revealing that it outperforms existing technologies in indicators such as loss function value, fitting effect, accuracy, precision, recall, and F1 score, with an accuracy of up to 96.30%. Additionally, this work explores the potential application of this model in talent cultivation in animation majors in higher education institutions, providing new teaching tools and methods to enhance students' innovation ability and practical skills. Therefore, this work offers a new technical means for artistic creativity evaluation and provides important references and guidance for innovation in higher education art education models and talent cultivation in the animation industry.