Innovation Clustering Analysis of Accurate Support Path for CSE under the Environment of Big Data

Facing the severe employment situation and social environment, the employment of college students has become a very important issue. The big data analysis plays a positive role in entrepreneurship, which can not only improve the success rate of entrepreneurial path selection, but also accumulate a lot of innovative practical experience for college students. Based on the importance of big data technology for entrepreneurial path, this paper proposes an innovative model of accurate support path for college students' entrepreneurship (CSE), in which the K-means algorithm is applied to the analysis of entrepreneurial support path. Finally, this paper makes an experimental analysis on the model, and the results show that K-means can greatly reduce the computational complexity of the algorithm, and the precision, recall, and F parameter of the model can be effectively improved. The model is of great significance in improving the feasibility of college students' entrepreneurial support policies.