In response to the problems that the new crown pneumonia epidemic may bring to doctors in schools of Chinese medicine, such as information overload, traumatic stress, changes in mentality, crisis of trust, safety when going abroad, and employment choices, explain from the perspective of medical teachers in Chinese medicine schools. The understanding of the epidemic and the major events during the epidemic, combined with the experience of the psychological impact of the past epidemic on the doctors in the school, to highlight the outstanding contributions of medical staff in the fight against the epidemic, and to emphasize the strong leadership of the party and the unity of the people during the fight against the epidemic. The huge role played by Chinese medicine in the fight against the epidemic is the principle. In recent years, cloud computing technology has been continuously developed and improved. Traditional infrastructure as a service has been unable to meet users' needs for cloud computing delivery capabilities. They hope that more and more traditional IT software will be delivered in the form of cloud services. This paper mainly studies the utility optimization and stable matching strategy of UDRN based on EH and limited character input, divides the nodes into multiple clusters, and implements the utility optimization strategy within the clusters. Model users and relays as energy buyers and energy sellers. First, when users choose relays as their candidates, the golden section method is used to obtain the best relay power to maximize user utility. Secondly, based on the principle of maximizing the utility of matching, a mutual preference matrix between users and relays is established. Finally, based on the mutual preference matrix, an improved UDRN stable matching algorithm is proposed through the less complex GS algorithm. This algorithm will play a great role in the psychological reconstruction of doctors after the epidemic.

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The number of autistic children and young people is rising rapidly across the world. Children with intellectual disabilities need special attention from trained experts. Educating them on improving their lifestyle is critical through the traditional teaching-learning environment. This study introduces an interactive educational framework that helps children with special needs have an improved and exciting learning process and explores the need to incorporate physical exercise into their everyday lives. Virtual Reality (VR) seeks more attention from autistic students. This research presents a Machine Learning-based Virtual Reality Application (ML-VRA) for Mentally Challenged Children and keeps the Human Behavior Analysis log files. Machine learning can predict the score of brain data ability. The visual short-term memory and visual-spatial memory are further assessed to identify students’ interaction with the VR application. Support Vector Regression prediction algorithm and Baseline Prediction algorithm are used to assess the score prediction for visual short memory and visual-spatial memory.Using an audio technology that allows autistic persons to hear various sounds, the cognitive method VRA instructs autistic children.Further, this study proposes a cognitive model for intellectual task processes and problem-solving using metacognitive architecture. Thus, children can acquire different levels of learning knowledge and skills. The case study performed on these model results with the highest prediction accuracy of 93.65%.