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Adoption of Human Personality Development Theory Combined With Deep Neural Network in Entrepreneurship Education of College Students

机译:采用人格开发理论与大学生创业教育中的深度神经网络相结合

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In this research, the probability matrix factorization (PMF) algorithm was introduced to optimize the deep neural network algorithm model with the purpose of studying the application value of personality development theory and deep learning neural network in college students’ entrepreneurship psychological education courses. Based on the personality development theory, a recommendation algorithm system for entrepreneurial projects under optimized deep neural network was established. A total of 518 college students from several universities were divided into an experimental group and a control group, with 259 students in each group. In addition to the normal courses of entrepreneurship psychology education, students in the experimental group were taught the entrepreneurship project recommendation system based on the optimized deep neural network designed in this research, while students in the control group were taught entrepreneurship psychology education normally. The intervention effect before and after entrepreneurship education was evaluated by the questionnaire of college students’ entrepreneurial intention and college students’ entrepreneurial mental resilience scale. The results demonstrate that the system recall rate and accuracy based on the algorithm in this research have been significantly higher than that of PMF algorithm and deep belief network (DBN) algorithm, and the difference is statistically significant ( p 0.05); the mean square error (MSE) of the proposed algorithm is significantly smaller than that of PMF algorithm and DBN algorithm, and the difference is statistically significant ( p 0.05); the improvement of entrepreneurial toughness, entrepreneurial strength, optimism, entrepreneurial possibility, and behavioral tendency of the experimental group after the test was significantly higher than that of the control group ( p 0.05). Therefore, compared with traditional algorithms, the proposed method for entrepreneurial projects based on the theory of personality development and the optimized deep neural network shows better performance, and it can effectively improve the entrepreneurial intention and psychological resilience of college students.
机译:在该研究中,引入了概率矩阵分解(PMF)算法以优化深度神经网络算法模型,以研究人格开发理论和深层学习神经网络在大学生创业心理教育课程中的应用价值。基于人格开发理论,建立了优化深神经网络下的创业项目推荐算法系统。来自几所大学的518名大学生分为实验组和一个对照组,每组259名学生。除了正常的创业心理学教育外,实验组的学生还教授了基于本研究中设计的优化的深神经网络的创业项目推荐系统,而控​​制组的学生通常是教育创业心理学教育。在大学生创业意图和大学生创业精神恢复力规模的调查问卷评估了创业教育前后的干预效果。结果表明,该研究基于算法的系统召回速率和精度显着高于PMF算法和深度信仰网络(DBN)算法的算法,差异是统计学意义(P <0.05);所提出的算法的平均方误差(MSE)显着小于PMF算法和DBN算法,差异是统计学意义(P <0.05);在试验后的实验组的创业韧性,创业强度,乐观,创业可能性和行为趋势的提高显着高于对照组(P <0.05)。因此,与传统算法相比,基于人格发展理论和优化的深神经网络的创业项目提出的方法表现出更好的性能,并能够有效地提高大学生的创业意图和心理复原力。

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