Firstly, the paper collects the first hand data by the questionnaire survey method, and then studies the relationship between university students' entrepreneurial intentions and entrepreneurial behaviors by artificial neural network based on the preliminary research results. The results show that among the 11 dimensions which are influenced the contemporary university students' Entrepreneurial intentions and entrepreneurial behavior, the impact of entrepreneurship education, entrepreneurship, entrepreneurial intentions, institutional environment, endowments are largest, the impact of the expected benefits, market opportunities and behavi-oral attitudes are smaller than the above five dimensions, the impact of cognitive, subjective norm, perceived behavioral control are smallest. The predicted result of improved BP Neural Network algorithm through genetic algorithm optimization are compared with the traditional BP neural network, it is found that optimization algorithm has better prediction accuracy which is improved nearly 10 percen-tage points.%通过设计调研问卷进行实地调研, 收集第一手资料数据, 在前期成果的基础上, 通过遗传算法优化的人工神经网络技术计量研究高校学生的创业意愿与创业行为间的关系. 结果表明影响当代大学生创业意愿与创业行为的11个维度中创业教育、 创业能力、 创业意愿、 制度环境、 禀赋5个维度对其影响度较大, 预期收益、 市场机会和行为态度等维度对当代大学生创业意愿与创业行为的影响次之, 认知、 主观规范、 行为知觉控制3个维度对当代大学生创业意愿与创业行为的影响最小. 并在此基础上, 将遗传算法优化的神经网络的预测结果与传统神经网络进行比较, 发现遗传算法优化的神经网络的预测效果更佳, 预测精确度提升了近10个百分点.
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