For the employment and entrepreneurship management of college students, the application of big data technology can effectively improve their work efficiency, that is, the support vector machine algorithm is applied to the employment and entrepreneurship management of college students. Based on deep learning technology, the deep neural network is constructed based on SVR and restrictive Boltzmann machine, namely, SVR-DBN, including theoretical derivation of model architecture, design and selection of model training algorithms, and the modeling steps and flow charts are given, and finally applied to the influence factor analysis. The multiangle comparison proves that the proposed depth model has excellent feature extraction ability and regression prediction. The results show that the algorithm has higher accuracy and has a 26% improvement over traditional algorithms. The research is of great significance to the improvement of the efficiency of employment and entrepreneurship management and the application of support vector machine algorithms.

中文翻译：

---

基于支持向量机算法的大学生创业管理机制

对于大学生的就业创业管理，大数据技术的应用可以有效提高他们的工作效率，即将支持向量机算法应用于大学生的就业创业管理。基于深度学习技术，构建基于SVR和限制性玻尔兹曼机的深度神经网络即SVR-DBN，包括模型架构的理论推导、模型训练算法的设计与选择，给出建模步骤和流程图，最后应用于影响因素分析。多角度比较证明，所提出的深度模型具有优异的特征提取能力和回归预测能力。结果表明，该算法具有更高的准确率，比传统算法提高了26%。该研究对提高就业创业管理效率和支持向量机算法的应用具有重要意义。