A Study on Optimizing the Learning Effectiveness of Copyright Knowledge in Music Copyright Education Using the Random Forest Algorithm

Abstract

With the continuous development of science and technology, the issue of learning copyright knowledge in music copyright education has become increasingly severe. In response to the problems highlighted above, this paper proposes a study on optimizing the effectiveness of copyright knowledge learning based on the random forest algorithm. By integrating music copyright education issues and three principles for constructing an evaluation system, this study establishes an evaluation indicator system for copyright knowledge learning effectiveness. Under the influence of the Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation Method, the evaluation indicators are quantified. Based on this, the Random Forest algorithm is utilized to optimize the quantified data of each evaluation indicator, followed by an exploration of the optimization effects of the Random Forest algorithm. The optimization values of the four algorithms are 3.9365, 3.7213, 3.7323, and 3.7088, respectively. It can be concluded that the optimization effect of the RF (random forest) algorithm is higher than that of the DT (decision tree algorithm), LSTM (long short-term memory network), and LR (logistic regression), verifying the practical applicability and application value of the research scheme proposed in this paper, which has practical strategic significance for improving music copyright education.