Visual Resolve of Modern Educational Technology Based on Artificial Intelligence under the Digital Background.

With the development of Internet technology and the arrival of the knowledge-driven era, the breadth and depth of educational informatization are increasing day by day. Educational technology is not only a subject but also a career adapted to education and teaching. The growth speed of modern educational technology and the size of its benefits determine its management level to a large extent. With new technologies, new ideas, and new social needs, it is difficult for new ideas, new thoughts, and new methods to make the traditional e-learning management to accommodate the demands of the new era. At present, the work efficiency of modern educational technology visualization systems is generally not high, and modern distance teaching has an increasing demand for management informatization. However, there is a lack of a management platform for distance education that adapts to organizational characteristics such as openness, dynamics, flexibility, individualization, and decentralization. Therefore, this study introduces machine learning and BP neural network, establishes a visual modern distance teaching management system model, and uses machine learning algorithms to learn the visual process. The experimental results show that the system efficiency after learning is higher, and the time required for visualization of different groups in the experiment is 14.32 s, 13.18 s, 12.27 s, and 13.64 s, respectively, which effectively improves the efficiency of visualization and reduces the consumption of human resources.

Application of Challenging Learning Based on Human-Computer Interaction under Machine Vision in Vocational Undergraduate Colleges.

Science and technology have progressed in recent years, the deepening of talent education, a challenging learning method of human-computer interaction has gradually emerged. Human-Computer Interaction (HCI for short) is the communication and interaction between humans and machines. This essay aims to apply the challenging learning combined with HCI to vocational undergraduate colleges. The GMM (that is Gaussian mixture model algorithm, commonly used in image recognition or speech recognition, etc.) algorithm is proposed in this essay to recognize students' actions. The effect of HCI is achieved by feeding back the recognized actions to the system. This essay selects 200 students from a vocational undergraduate college for challenging learning (that is, a comprehensive teaching method aiming at students' autonomous learning by stimulating students' interest in learning). The challenging learning designed in this essay is divided into 15 weeks, the task chain contains a total of 196 tasks, and the learning time is 138 h. This essay analyzes the application effects of liberal arts, male and female students, and different grades. The results show that the overall average completion rates of learning tasks for freshman, sophomore, and junior students are about 70%, 75%, and 85%, respectively, and the overall average scores for challenging learning are about 70, 78, and 83. The overall completion rate of weekly tasks for boys and girls is about 68% and 70%, and the overall average score is about 70 points and 75 points. The weekly task completion rate of liberal arts students is generally above 75%, and the overall average score is about 70 points. The overall completion rate of science students is less than 75%, and most of the learning scores are higher than 75 points. In addition, the average accuracy of the GMM algorithm for face and gesture recognition is 90% and 87%. The average frequency of students using HCI is about 320 times a day; the average score of students' experience effect of HCI is about 80 points. It may be stated that the HCI demanding learning strategy proposed in this study worked well and has achieved satisfactory learning results in the application of vocational undergraduate colleges.