<p>Music colleges in China currently face significant challenges in innovation and entrepreneurship education (IEE), including inadequate instructional support systems and fragmented teaching methods. These issues are particularly pronounced in aesthetic education courses, where scientific assessment and personalized guidance for students’ entrepreneurial competence remain underdeveloped. To address this gap, this study proposes and validates an auxiliary instructional system based on the back propagation neural network (BPNN) model aimed at improving the precision and effectiveness of IEE within aesthetic education. The study investigates whether the BPNN model can accurately model and assess students’ entrepreneurial abilities to support pedagogical optimization. Targeting graduates from music colleges in the Xi’an region, this study constructs a competence evaluation framework comprising four primary indicators and twelve secondary indicators, gathering 444 valid questionnaire responses. Using this data, the BPNN model is designed and trained to predict and provide feedback on students’ innovation and entrepreneurship competencies. The model achieves a maximum relative error of only 1.64% between predicted and actual outputs, demonstrating strong accuracy and practical viability. Results highlight the theoretical and applied value of leveraging deep learning for entrepreneurial competence assessment in the integration of arts education and IEE. However, the current evaluation framework requires further refinement to better meet the evolving demands of specialization and industrialization.</p>