In the volatile landscape of copyright, portfolio optimization presents a substantial challenge. Traditional methods often falter to keep pace with the rapid market shifts. However, machine learning algorithms are emerging as a innovative solution to optimize copyright portfolio performance. These algorithms interpret vast datasets to identify corr