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The research focuses on the application of positive psychology theory, and studies the educational utility of national films by using deep learning (DL) algorithm. As an art form leading China's film and TV industry, national films have attracted the interest of many domestic scholars. Meanwhile, researchers have employed various science and technologies to conduct in-depth research on national films to improve film artistic levels and EDU-UTL. Accordingly, this paper comprehensively studies the EDU-UTL of national films using quality learning (Q-Learning) combined with DL algorithms and educational psychology. Then, a deep Q-Learning psychological model is proposed based on the convolutional neural network (CNN). Specifically, the CNN uses the H-hop matrix to represent each node, and each hop indicates the neighborhood information. The experiment demonstrates that CNN has a good effect on local feature acquisition, and the representation ability of the obtained nodes is also powerful. When K = 300, the psychological factor Recall of Probability Matrix Decomposition Factorization, Collaborative DL, Stack Denoising Automatic Encoder, and CNN-based deep Q-Learning algorithm is 0.35, 0.71, 0.76, and 0.78, respectively. The results suggest that CNN-based deep Q-Learning psychological model can enhance the EDU-UTL of national films and improve the efficiency of film education from the Positive Psychology perspective.

In the course of consumer behavior, it is necessary to study the relationship between the characteristics of psychological activities and the laws of behavior when consumers acquire and use products or services. With the development of the Internet and mobile terminals, electronic commerce (E-commerce) has become an important form of consumption for people. In order to conduct experiential education in E-commerce combined with consumer behavior, courses to understand consumer satisfaction. From the perspective of E-commerce companies, this study proposes to use artificial intelligence (AI) image recognition technology to recognize and analyze consumer facial expressions. First, it analyzes the way of human–computer interaction (HCI) in the context of E-commerce and obtains consumer satisfaction with the product through HCI technology. Then, a deep neural network (DNN) is used to predict the psychological behavior and consumer psychology of consumers to realize personalized product recommendations. In the course education of consumer behavior, it helps to understand consumer satisfaction and make a reasonable design. The experimental results show that consumers are highly satisfied with the products recommended by the system, and the degree of sanctification reaches 93.2%. It is found that the DNN model can learn consumer behavior rules during evaluation, and its prediction effect is increased by 10% compared with the traditional model, which confirms the effectiveness of the recommendation system under the DNN model. This study provides a reference for consumer psychological behavior analysis based on HCI in the context of AI, which is of great significance to help understand consumer satisfaction in consumer behavior education in the context of E-commerce.

This study combines the discovery methods and training of innovative talents, China’s requirements for improving talent training capabilities, and analyses the relationship between the number of professional enrollments in colleges and universities and the demand for skills in specific places. The research learns the characteristics and training models of innovative talents, deep learning (DL), neural networks, and related concepts of the seasonal difference Autoregressive Moving Average (ARMA) Model. These concepts are used to propose seasonal autoregressive integrated moving average back propagation (SARIMA-BP). Firstly, the SARIMA-BP artificially sets the weight parameter values and analyzes the model’s convergence speed, superiority, and versatility. Then, particle swarm optimization (PSO) algorithm is used to pre-process the model and test its independence. The accuracy of the model is checked to ensure its proper performance. Secondly, the model analyzes and predicts the relationship between the number of professional enrollments of 10 colleges and universities in a specific place and the talent demand of local related enterprises. Moreover, the established model is optimized and tested by wavelet denoising. Independent testing is done to ensure the best possible performance of the model. Finally, the weight value will not significantly affect the model’s versatility obtained by experiments. The prediction results of professional settings and corporate needs reveal that: there is a moderate correlation between professional locations and corporate needs; colleges and universities should train professional talents for local enterprises and eliminate the practical education concepts.

More and more schools begin to design simulation technology based on virtual imaging technology (VIT) and virtual reality (VR) in their course contents. In particular, among these technical courses, there is a need to first strengthen the Film and Television Production (FTP) education in higher institutions. This article aims to study the impact of VRT, VR, and Internet of things (IoT) technology on FTP courses and audience psychology in higher institutions under the era of intelligent multimedia. How to use emerging VR technology to promote the psychological wellbeing of students or patients has become a new research direction, the exploration of which has a far-reaching significance for the applications of the related technologies. First, the principle and applications of VR and IoT technology are described. Thereon, the deep learning (DL)-based training model is used to analyze the postproduction (PP) of VR-based Sand Table game, and the function and effect of the designed game model are discussed. Subsequently, VR-based Sand Play Therapy (SPT) is applied to mentally ill patients to obtain its therapeutic effect. The results show that the designed VR-based Sand Table game model can be used to treat mentally ill patients and alleviate their negative psychological states. Meanwhile, the Test Anxiety Scale (TAS) scores prove the significant therapeutic effect of the designed game model on the mental problems of patients. Therefore, VR-based psychological SPT can be applied in the stress relief of students and the treatment of mentally ill patients, as well as alleviate their mental health problems. This research provides a new direction and some theoretical support for the application field of VR technology.

This study aims to explore new educational strategies suitable for the mental health education of college students. Big data and artificial intelligence (AI) are combined to evaluate the mental health education of college students in sports majors. First, the research status on the mental health education of college students is introduced. The internet of things (IoT) on mental health education, a structure based on big data and convolutional neural network (CNN), is constructed. Next, the survey design and questionnaire survey are carried out. Finally, the questionnaire data are analyzed and compared with the mental health status under traditional education. The results show that the CNN model has good accuracy and ability to distinguish symptoms, so it can be applied to the existing psychological work in colleges. In the symptom comparison survey, under the traditional education and big data network, the number of college students with mild mental health problems is found to be 158 (84.9%) and 170 (91.4%), respectively. It indicates that the number of college students with moderate mental health problems decreases significantly. In the comparative investigation of the severity of mental problems, the number of students with normal mental health, subhealth, and serious mental health problems under the background of traditional mental health education is 125 (67.2%), 56 (30.1%), and 5 (2.7%), respectively. The mental health status of college students under the influence of big data networks on mental health education is better than that of traditional mental health education. There are 140 students with normal mental health, a year-on-year increase of 16.7%. In the comparative survey of specific mental disorders, students with obsessive-compulsive symptoms under traditional mental health education account for 22.0% of the total sample, having the largest proportion. In the subhealth psychological group under the big data network on mental health education, the number of hostile students decreases by 7, which is the psychological factor with the most obvious improvement. Hence, the proposed path of mental health education is feasible.

The purpose is to strengthen the life education of contemporary college students and give better play to the vital role of life education in preventing college students’ mental diseases. Specifically, it discusses the role of dance therapy (DT) in College Students’ Life Education (CSLE). Firstly, based on educational psychology (EP), this manuscript analyzes the relevant theoretical concepts of EP and life education and discusses the importance of life education to contemporary college students. Secondly, following a Questionnaire Survey (QS) and using deep learning (DL) Convolutional Neural Network (CNN) and Facial Emotion Recognition (FER), this manuscript reviews and examines the CSLE’s current situation and the DT effect. Research findings are summarized combined with the QS results and scores of 20 subjects before and after five activities in 3 months. (I) After DT intervention, the positive dimensions of college students’ life values have improved, especially self-development and dedication, and their quality of life is refined. Thus, DT group counseling proves the positive role of DT in CSLE. (II) After DT intervention, 96.5% of the members think DT is effective. Therefore, EP-based DT is more effective and scientific in CSLE. The research findings provide a DT-based teaching concept for CSLE, explore the feasibility and effectiveness of life education, and enrich the DT scheme of CSLE. The research provides a practical reference for further applying DT in college students’ psychological education.

Globalization and informatization are reshaping human life and social behaviors. The purpose is to explore the worldwide strategies to cultivate international talents with a global vision. As a global language with the largest population, English, and especially its learning effect, have always been the major concerns of scholars and educators. This work innovatively studies the combination of immersion-based English teaching with virtual reality (VR) technology. Then, based on the experimental design mode, 106 students from a Chinese school were selected for a quasi-experimental study for 16 weeks (3 h a week, and 48 h in total). The collected data were analyzed by computer statistical software, and hypotheses are verified. The results showed that there is a significantly positive correlation between VR and immersion-based language teaching (0.851, p < 0.01). There is a significantly positive correlation between immersion-based language teaching and academic achievement (0.824, p < 0.01), and VR is positively correlated with learning outcome (LO) (0.836, p < 0.01). Compared with other state-of-art research methods, this work modifies the students’ oral test through the analysis and comparison with the system database, and the students’ learning effect is greatly improved. Finally, some suggestions are put forward according to the research results to provide an experimental reference for English teachers and future linguistics teaching.

The artificial neural network (ANN) is employed to study children’s psychological emotion recognition to fully reflect the psychological status of preschool children and promote the healthy growth of preschool children. Specifically, the ANN model is used to construct the human physiological signal measurement platform and emotion recognition platform to measure the human physiological signals in different psychological and emotional states. Finally, the parameter values are analyzed on the emotion recognition platform to identify the children’s psychological and emotional states accurately. The experimental results demonstrate that the recognition ability of children aged 4–6 to recognize the three basic emotions of happiness, calm, and fear increases with age. Besides, there are significant age differences in children’s recognition of happiness, calm, and fear. In addition, the effect of 4-year-old children on the theory of mind tasks is less than that of 5- to 6-year-old children, which may be related to more complex cognitive processes. Preschool children are experiencing a stage of rapid emotional development. If children cannot be guided to reasonably identify and deal with emotions at this stage, their education level and social ability development will be significantly affected. Therefore, this study has significant reference value for preschool children’s emotional recognition and guidance and can promote children’s emotional processing and mental health.

The study expects to find a better way to improve the teaching quality of the education of college students' outlook on life, based on the theory of educational psychology. First, the relevant theories of positive psychology are introduced and expounded, and the importance of the education of college students' outlook on life is analyzed. Second, the current situations of college students' outlook on life and the education of their outlook on life are investigated through a questionnaire survey, and the problems presented in the questionnaire are analyzed. Then, the correlation between positive psychology and the education of college students' outlook on life is explored, and their mutual connection is analyzed. The results are as follows: 77.4% of the college students have periodical aims and work hard for them; 80.8% of the students think that the realization of life goals rely on hard work, accounting for the largest proportion; when they encounter setbacks, more than 80% of the students choose to work hard to overcome them; 69.2% students think that their outlook on life comes from self-learning and exploration. According to college students' outlook on life in China and other countries, there are many problems in the education of college students' outlook on life, and the teaching quality of the education of college students' outlook on life is backward. The combination of positive psychology and college students' education of college students' outlook on life under the theory of educational psychology provides new ideas and ways for college students' education of college students' outlook on life. The conclusion of this study promotes the innovation of the education of college students' outlook on life.

Educational psychology focuses on the laws of change in the knowledge, skills, and individual psychology of the educatees in the process of education and teaching. Writing teaching is a key and difficult point in literature teaching. Nowadays, it is common for students to be afraid and tired of writing in school literature education. In view of these problems, the present work optimizes the teaching mode of writing from the perspective of reconstructing the writing subject. Through literature research and interdisciplinary analysis, a questionnaire is designed to analyze the literary situation and the reconstruction of writing subjects in literary education. The questionnaire is aimed at three aspects, namely the hidden educational effect of teachers’ personality charm, the influencing factors of students’ psychology and students’ learning effect, and the influencing factors of psychology of the communication between teachers and students and teachers’ teaching effect. Then, the changes of students’ performance in literary class in these three aspects before and after using the teaching strategy of writing subject reconstruction are analyzed. Finally, the changes of students’ grades in the literary course are investigated. In this experiment, a total of 400 questionnaires were distributed, and a total of 389 questionnaires were collected. The survey results show that the number of students who feel the classroom atmosphere is active increases by 10%, the number of students who listen carefully and take notes increases by 7%, and 45% of students have improved their grades. Besides, after the implementation of the teaching strategy, most students change their attitude to the literature course, become more active, and significantly improve their motivation for learning. This study has a certain reference value for the analysis of literary situations and the reconstruction of writing subjects in literary education from the perspective of educational psychology.

This study aims to explore the current situation and strategy formulation of sports psychology teaching in colleges and universities following adaptive learning and deep learning under information education. The informatization in physical education, teaching methods, and teaching processes make psychological education more scientific and efficient. First, the relevant theories of adaptive learning and deep learning are introduced, and an adaptive learning analysis model is implemented. Second, based on the deep learning automatic encoder, college students’ sports psychology is investigated and the test results are predicted. Finally, the current situation and development strategy of physical education in colleges and universities are analyzed. The results show that when the learning rate is 1, 0.1, and 0.01, there is no significant change in the analysis factors of recall, ndcg, item_coverage, and sps. When the learning rate is 1, their analysis factors change obviously, and it is calculated that the optimal learning rate of the model is 1. And the difficulty of the recommended test questions by using the sports psychology teaching method based on adaptive learning and deep learning is relatively stable. The test questions include various language points of sports psychology. Compared with others methods, adaptive learning and deep learning can provide comprehensive test questions for sports psychology teaching. This study provides technical support for the reform of sports psychology teaching in colleges and universities and contributes to optimizing the information-based teaching mode.

The quality of Innovation and Entrepreneurship Education (IEE) in higher institutions is closely related to the degree to which the undergraduates (UGs) absorb relevant innovation and entrepreneurship knowledge and their entrepreneurial motivation. Thus, an effective Evaluation of Educational Quality (EEQ) is essential. In particular, fault tree analysis (FTA), a common EEQ approach, has some disadvantages, such as fault data reliance and insufficient uncertainties handleability. Thereupon, this article first puts forward a theoretical model based on the deep learning (DL) method to analyze the factors of IEE quality; consequently, based on the traditional FTA, fuzzy fault tree analysis (FFTA) is proposed to evaluate the reliability of IEE classroom teaching for college teachers and students. Finally, based on the top event of entrepreneurial teaching failure, the hyper-ellipsoid model is implemented to restrict the interval probability of basic events and describe the deviation of uncertain events. Furthermore, the model accuracy is verified by a questionnaire survey (QS), based upon which the factors of IEE quality are analyzed. The results show that the designed QS has good reliability, validity, and fitness; the path coefficients of cooperative ability to critical thinking and innovative thinking are 0.9 and 0.66, respectively, indicating that the students’ cooperative ability plays a vital role in the classroom teaching. By calculation, the probability of “teaching failure” in entrepreneurial classroom teaching is 0.395, 3, 0.462, and 5. To sum up, the proposed method can effectively and quantitatively evaluate the quality of IEE in higher institutions, thus providing a certain basis for formulating relevant improvement strategies. The purpose is to provide important technical support for improving the IEE quality.

This study aims to explore the entrepreneurial psychology of physical Education (PE) students under the “Internet+”environment, to cultivate and improve the entrepreneurial consciousness of PE students, taking the realization of students’ sense of self-efficacy as an intermediary factor. The new educational technology in modern PE is analyzed first. Specifically, the motion sensing technology based on human-computer natural interaction can be used for training, so that learners can effectively improve their physical skills. Subsequently, the current entrepreneurial situation of PE majors is discussed, with 188 students from Tianjin University of Sport and Guangzhou Sport University selected as research subjects. It is found that 62.2% of students have never been exposed to online entrepreneurship, and they are more afraid of entrepreneurial risks. In terms of entrepreneurial motivation, most students choose to start a business because of “personal ideals,” and only 40 people choose to start a business because of economic factors. There is a significant positive correlation between entrepreneurial self-efficacy and entrepreneurial intention of college students majoring in PE, and the correlation coefficient is 0.488. At present, the teaching mode of sports universities focuses on the teaching of professional courses. However, students generally believe that the professional knowledge learned is not useful for future entrepreneurship. The entrepreneurial self-efficacy of college students tends to be positive, and there are notable differences in the entrepreneurial self-efficacy between boys and girls. The regression analysis of entrepreneurial self-efficacy and entrepreneurial intention of college students shows that entrepreneurial self-efficacy can effectively predict entrepreneurial intention. This research promotes the innovation and development of the sports industry under the background of “Internet+”.

The investigation into entrepreneurial behavior and model innovation of new ventures based on news communication aims to reinforce the market competition strength and improve the performance level of new ventures to meet the urgent needs of transformation and upgrading. Based on the theoretical basis of news communication and social cognition theory, a theoretical model is constructed to analyze the relationship between entrepreneurial behavior, innovation mode, and entrepreneurial self-efficacy (ESE) and to provide a reference for the implementation of entrepreneurial behavior of new ventures. Evidently, taking individual factors as antecedent variables to verify their impact on internal entrepreneurial behavior extends the scope of research on corporate entrepreneurship and also provides significant managerial implications for the promotion of entrepreneurial activities.