An innovation and entrepreneurship education model centered around Timmons’ entrepreneurial process is suggested in this study after an initial analysis of applied bachelor universities, innovation and entrepreneurship education, and innovation and entrepreneurship courses. Second, a neural network with BP and an artificial neural network is used to build an assessment system for the efficiency of school innovation and entrepreneurship courses. Finally, examples were used to quantitatively study data on the approval of innovation and entrepreneurship classes and students’ graduation selections. The findings reveal that satisfaction with the innovation and entrepreneurship course may be successfully assessed using data analysis techniques because there is just a variance of 0.037 between the input assessment, with the lowest average score of 4.938 and the multidimensional experience assessment, with the highest average score of 4.975.