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.
Under the background of mass entrepreneurship and innovation, innovative entrepreneurship research is urgently needed for entrepreneurs. The aim was to explore the innovative entrepreneurship consciousness of new entrepreneurs. First, the regional competitive advantage theory is discussed. The research method of questionnaire survey is combined with statistical analysis to obtain relevant research data. Quantitative standards are used to measure the impact of regional advantages and policy support on entrepreneurs’ innovation and entrepreneurship. The policy content analysis and questionnaire survey are applied to discuss the impact of regional competitive advantage, educational psychology, innovation and entrepreneurship policy, and entrepreneurs’ innovation consciousness. Meantime, the policies related to entrepreneurs’ innovation and entrepreneurship are further explored. The results show that the proportions of young entrepreneurs suffering anxiety and depression are 29.4 and 27.5%, respectively, which are significantly higher than the national average. Besides, in the test, F=23.11, p<0.005, which indicates that all proposed hypotheses are valid. The results suggest that the overall mental health level of young new entrepreneurs is not high, and their consciousness of innovation and entrepreneurship needs to be strengthened. Under the wave of mass innovation and entrepreneurship, the research results can strengthen entrepreneurs’ innovative entrepreneurship consciousness and may have great theoretical and practical significance for improving and optimizing government innovation and entrepreneurship policies.
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