LAUSR

To promote the development of the rural economy and improve entrepreneurship education in colleges and universities, college students’ willingness and behavior toward rural tourism entrepreneurship were investigated in this study. First of all, based on the previous research results, the influencing factor model was determined for college students’ entrepreneurial intention. Second, a questionnaire survey was made to collect data from a university in Xi’an City. Finally, the artificial neural network (ANN), improved by a genetic algorithm (GA) based on an artificial intelligence network, was used to study the relationship between college students’ entrepreneurial intention and behavior, and the simulation was carried out on MATLAB2013b software. The results show that the average evaluation accuracy is 81.13% for 60 groups of data using the unmodified back propagation neural network (BPNN) algorithm, while the average evaluation accuracy is 92.17% for the BPNN algorithm improved and optimized by GA, with an ascent of 11.04%. Therefore, the BPNN algorithm improved and optimized by GA is better than the unmodified BPNN algorithm; It is also feasible and effective in the analysis of influencing factors of college students’ entrepreneurial intention and behavior. The research provides a basis for colleges and universities to carry out entrepreneurship education on a large scale and to cultivate their innovative talents.