With the rapid development of economy and society, the employment pressure of college students increases year by year. In order to improve the employment competitiveness of college students, innovation and entrepreneurship education has become an important part of higher education. This paper constructs a big data platform for innovation and entrepreneurship education in colleges and universities, which includes data collection sources, data analysis, and data prediction. Apriori algorithm is used to correlate positive-willing students with negative-willing students, and K-means algorithm is used to assess students’ entrepreneurial thinking and explore new ways to personalize innovation and entrepreneurship education. The results of the study show that 70.3% of students believe that they choose to start their own business after graduation, and that being able to grasp entrepreneurial policies in a timely manner during college is an important reason for their decision to start their own business, and that the combined effect of motivational and resistance factors affects the willingness of college students to improve their entrepreneurial ability through participation. The results obtained by using the K-means algorithm provide colorful and targeted career planning development paths for students with different characteristics, which provides a good development direction for the personalized teaching method of innovation and entrepreneurship education.
