Analysis of Data Mining Mode of Art History Course Based onFNN

Abstract

With the continuous development of information technology, the teaching of art history courses is also changing quietly. In recent years, the museum field teaching method that emphasizes “original works” in the teaching of art history courses has great room for improvement. This paper firstly analyzes the teaching design, teaching content and other issues by sorting out the development background of the art history MOOC course, and puts forward some research and educational view-points on the MOOC course. Teaching workers should focus on the reform of information application teaching in art history teaching, and advocate making full use of modern information technology to establish an art history teaching environment that is convenient for teachers and students to to communicate and interact. Finally, based on the fuzzy neural network technology, the data mining of the art history course is carried out, and it is concluded that FNN, as a nonlinear adaptive dynamic system, has the advantage of extracting the internal characteristics of information through self-learning, and can approximate any real situation on any closed subset with arbitrary precision. Continuous function. Through the learning of the neural network system, the knowledge contained in a large amount of data is compressed into the weights between nodes. On the basis of theoretical and image research, teachers and students should contact a large number of original works, conduct field inspections, and deeply understand the essence of Western art. and the cultural drivers behind it.