[Retracted] Application of Support Vector Machine Model Based on Machine Learning in Art Teaching

Table 1

Art teaching quality evaluation index system.

Primary indicator	Secondary indicators	Label
Teaching objectives	Express clearly and specifically	X1
	It is very hierarchical and scientific	X2
	Fully feasible and operable	X3
	Reflect students’ personality and professional characteristics	X4
Teaching content	To impart the spiritual essence of art to students	X5
	Guide students to understand new achievements and trends in art	X6
	High degree of connection between technique operation and art theoretical knowledge	X7
	The depth and breadth of art theory in different directions are well balanced	X8
Teaching methods	Pay attention to the teaching of learning methods and the inspiration of creative thinking	X9
	Comprehensive cross-over of knowledge in all aspects of art	X10
	Design different teaching methods according to the characteristics of art courses	X11
	Timely reflection on the validity of each teaching link	X12
Teaching effect	Students have a comprehensive grasp of the concepts and skills in art teaching	X13
	Students develop artistic thinking, artistic observation, and artistic creation ability	X14
	Students develop positive emotions and attitudes towards art	X15
	The planning and effectiveness of teaching activities are consistent with the goals	X16