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首页> 外文期刊>Frontiers in Psychology >Adoption of Human Personality Development Theory Combined With Deep Neural Network in Entrepreneurship Education of College Students
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Adoption of Human Personality Development Theory Combined With Deep Neural Network in Entrepreneurship Education of College Students

机译：采用人格发展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 order to study the application value of personality development theory and deep learning neural network in college students' entrepreneurship psychological education courses, the probability matrix factorization (PMF) algorithm is introduced to optimize the deep neural network algorithm model. Based on the personality development theory, a recommendation algorithm system for entrepreneurial projects under optimized deep neural network is established. Then 128 students of Northeastern University are divided into experimental group and control group. In addition to the normal courses of entrepreneurship psychology education, students in the experimental group are taught the entrepreneurship project recommendation system based on optimized deep neural network designed in this research, while students in the control group are taught entrepreneurship psychology education normally. The intervention effect before and after entrepreneurship education is evaluated by the questionnaire of college students' entrepreneurial intention and college students' entrepreneurial mental resilience scale. The results show 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 (P0.05); 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 (P0.05); the improvement of entrepreneurial toughness, entrepreneurial strength, optimism, entrepreneurial possibility, and behavioral tendency of the experimental group after test was significantly higher than that of the control group (P0.05), which shows that compared with traditional algorithms, the performance of recommendation algorithm for entrepreneurial projects based on the theory of personality development and the optimized deep neural network is better, which can effectively improve the entrepreneurial intention and psychological resilience of college students.
机译：为研究大学生人格发展理论和深度学习神经网络的应用价值，介绍了概率矩阵分解（PMF）算法优化了深度神经网络算法模型。基于人格开发理论，建立了优化深神经网络中创业项目推荐算法系统。然后，东北大学128名学生分为实验组和对照组。除了正常的创业心理学教育课程外，实验组的学生都是根据本研究中设计的优化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企业家心理学教育。企业家教育前后的干预效应是由大学生的创业意图和大学生创业精神恢复力规模的调查问卷评估。结果表明，该研究基于该研究的算法的系统回忆速率和精度显着高于PMF算法和深度信仰网络（DBN）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）;试验后，实验组的创业韧性，创业强度，乐观，创业性能和行为趋势的提高显着高于对照组（P <0.05），表明与传统算法相比，建议的表现基于人格开发理论的企业家项目算法，优化的深度神经网络更好，可以有效提高大学生的创业意图和心理复原力。

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