The application of information fusion technology in the cultivation of talents in fine arts majors

Abstract

In this paper, we use information fusion technology to describe the problem formalization of art major talent cultivation and obtain the similarity coefficients and probability matrices of art major talent cultivation by calculating the ability level of art majors. On this basis, the art major talent cultivation path is proposed from two aspects: the planning of the art major cultivation path and the recommendation of a personalized art major academic talent cultivation path. The data set is selected, the experimental environment is determined, and the data analysis software is used to analyze the example of the ant colony optimization algorithm-based talent cultivation path planning for fine arts majors and the CNN-based talent cultivation path recommendation for fine arts majors. The results show that the teaching effect of mastering knowledge points is poor, and the duration has a more obvious change (598s), which confirms that the ant colony optimization algorithm can optimize the teaching duration of video knowledge points according to the situation of talent cultivation path of fine arts majors. CNN, in solving the problem of recommending the talent cultivation path of fine arts majors, compares with the genetic algorithm GA (0.662) and the original ant colony algorithm ACO (0.665); the most personalized learning path recommended is not only more suitable for the user’s learning style, but also in line with the actual learning sequence, and thus can help users efficiently achieve their career goals.