The construction of modern aesthetic education innovative talent cultivation mode in universities based on big data technology

Abstract

Abstract To strengthen the comprehensive literacy of college students and have a correct aesthetic concept, the cultivation of innovative talents in modern college aesthetic education under big data is proposed. The mixed-filtered college aesthetic training data were used to calculate the prediction function, loss function, and regression parameters using a logistic regression model. The training sample probabilities are derived from the maximum likelihood function to predict the unmade scores in the teacher’s scoring matrix, which is trained to fit the original scoring matrix. The behavior of each student is divided into a corresponding label, and the aesthetic education courses with historical preferences of students in the same category are recommended to the target students in that category. By iteratively updating the values of each cluster center one by one, the data with high similarity are clustered into one class, and the distances of all the cluster centers are recalculated to alleviate the data sparsity problem and scalability problem of logistic regression. The information of the training set is used as input data, and the numerical type indicators are used as the basis for judgment to obtain the satisfaction situation of students with the recommended results, and the indicators for visually evaluating the gap between the predicted and real ratings of the system and the accurate and complete data on the cultivation of innovative talents in modern university aesthetic education are counted. The analysis results show that under the big data technology, the physical education courses in colleges and universities carry out the education of the concept of beauty, the innovative form is better, and the percentage of the concept of strength and beauty reaches 69%, strengthening the ability of college students to feel beauty.