The rapid development of information technology affects people’s living habits. The new media based on information technology not only makes the traditional media suffer a serious impact but also breaks the constraints of traditional education methods. The orderly development of traditional culture education and mental health education activities in colleges and universities can play an important role in optimizing the ideological and cultural concepts and adjusting the psychological state of college students. It provides support and guarantees for college students to form scientific, cultural understanding concepts and good psychological quality. The gradual maturity of new media information transmission and distribution technology has profoundly influenced and changed the traditional cultural knowledge and mental health teaching work in colleges and universities, which has caused an extremely far-reaching real impact. This paper analyzes the problems of the traditional culture education and mental health education in colleges and universities under the new media environment. Then, the influence of new media on college students’ traditional culture education and mental health education and its reasons are analyzed, and the corresponding countermeasures are put forward.
In order to improve the detection and identification ability of sports injury ultrasound medicine, a segmentation method of sports injury ultrasound medical image based on local features is proposed, and the research on the sports injury ultrasound medical detection and identification ability is carried out. Methods of the sports injury ultrasound medical image segmentation model are established; the sports injury ultrasound medical image information is enhanced by using the sports skeletal muscle block matching technology; the image features are extracted; and the characteristics of sports injury ultrasound medical images are analyzed by CT bright spot feature transmission. In detail, combined with the deep convolutional neural network training method, the extracted sports injury points are automatically detected for sports injury ultrasound medical images, and the sports injury ultrasound medical image segmentation is realized. The simulation results show that this method has high accuracy for sports injury ultrasound medical image segmentation, the error value can be controlled below 0.103, and finally, the effect of zero error is achieved. It is confirmed that the method proposed in this paper has high resolution and accuracy for sports injury point detection and has strong practical application ability.
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