Mechanism and Sustainable Development of the Practical Ability Enhancement Mechanism for Art Students in Colleges and Universities Based on Internet Plus

Abstract

Currently, art students in colleges and universities see computer technology as a tool for daily living and enjoyment, but often fail to incorporate it into their studies. This paper, introduces the mechanism of the practical ability enhancement mechanism for art students in colleges and universities based on internet plus. The work aims to enhance students' creative imagination, aesthetic skill, and quality, as well as to support their overall growth. Initially, the input images gathered from Art Images: Drawing/Painting/Sculptures/Engravings data set collected image is pre processed by using Privacy-Preserving Distributed Kalman filtering (PP-DKF) preprocessed the image to enhance image clarity. Then the preprocessed output is fed to the Mix Style Neural Networks (MSNN) predicting the grades of art students in colleges and universities based on the internet plus. The weight parameter of the MSNN optimized with secretary bird optimization algorithm (SBOA) for accurate prediction. The proposed MDEACUI-MSNN-SBOA proposed is implemented on the Python working platform. The performance of the proposed MDEACUI-MSNN-SBOA approach attains 23.52%, 21.72%, and 24.92%higher accuracy; 23.52%, 22.72%, and 21.92%higher Precision; and 20.74%, 28.01% and 23.28%higher recall compared with existing methods such as Quantitative Enhancement of University Students’ Employability Deep Learning Digital Era (QEUEDDE-DFNN), Entrepreneurship education computer-aided instruction for college Music using convolution neural network (EECIC-CNN)  and  Application of Art in Colleges and Universities Based on BP Neural Network Algorithm (AACU-BPNN) respectively.