With the continuous development of the economy, people pay more attention to spiritual needs, so the cultural industry has developed by leaps and bounds. The development of Chinese culture and art is transforming toward internationalization and industrialization, and new art forms and styles are constantly emerging. In addition, China's online education is currently in a critical period of transformation. The form of online education is comprehensively deepened, first of all in the field of basic vocational education. The second is the training of students in basic education. At present, the art design teaching courses in Chinese schools have not been fully digitized, and they are faced with the problems of outdated teaching materials, slow updating speed and high purchase cost. Based on this, this study designs an online art teaching system by introducing computer image segmentation technology. Through analysis, it can be concluded that the core algorithm of the system is superior to FCM, FCM_S1 and FCM_S2, etc., and is excellent in division entropy, coefficient and accuracy, and can fully drive the system designed in this paper. The main functional modules of the system include learning guidance, course guidance and teaching resources. Through the simulation experiment, the online art design teaching system of the Internet can fully meet the needs of many students to access through the intranet at the same time, and the external network can meet 500 concurrent users without pressure. So, the overall system has good performance. This paper introduces the computer image segmentation technology into the field of art design teaching and designs a kind of effective Internet online system.
Keyphrases
- deep learning
- health information
- medical students
- hiv infected
- healthcare
- social media
- antiretroviral therapy
- convolutional neural network
- quality improvement
- working memory
- mental health
- squamous cell carcinoma
- computed tomography
- radiation therapy
- magnetic resonance
- virtual reality
- neural network
- locally advanced
- rectal cancer