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Adoption of Human Personality Development Theory Combined With Deep Neural Network in Entrepreneurship Education of College Students
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 psycho...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7361131/ https://www.ncbi.nlm.nih.gov/pubmed/32733313 http://dx.doi.org/10.3389/fpsyg.2020.01346 |
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author | Chen, Zhen Yu, Xiaoxuan |
author_facet | Chen, Zhen Yu, Xiaoxuan |
author_sort | Chen, Zhen |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7361131 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-73611312020-07-29 Adoption of Human Personality Development Theory Combined With Deep Neural Network in Entrepreneurship Education of College Students Chen, Zhen Yu, Xiaoxuan Front Psychol Psychology 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. Frontiers Media S.A. 2020-07-08 /pmc/articles/PMC7361131/ /pubmed/32733313 http://dx.doi.org/10.3389/fpsyg.2020.01346 Text en Copyright © 2020 Chen and Yu. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychology Chen, Zhen Yu, Xiaoxuan Adoption of Human Personality Development Theory Combined With Deep Neural Network in Entrepreneurship Education of College Students |
title | Adoption of Human Personality Development Theory Combined With Deep Neural Network in Entrepreneurship Education of College Students |
title_full | Adoption of Human Personality Development Theory Combined With Deep Neural Network in Entrepreneurship Education of College Students |
title_fullStr | Adoption of Human Personality Development Theory Combined With Deep Neural Network in Entrepreneurship Education of College Students |
title_full_unstemmed | Adoption of Human Personality Development Theory Combined With Deep Neural Network in Entrepreneurship Education of College Students |
title_short | Adoption of Human Personality Development Theory Combined With Deep Neural Network in Entrepreneurship Education of College Students |
title_sort | adoption of human personality development theory combined with deep neural network in entrepreneurship education of college students |
topic | Psychology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7361131/ https://www.ncbi.nlm.nih.gov/pubmed/32733313 http://dx.doi.org/10.3389/fpsyg.2020.01346 |
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