In the volatile landscape of copyright, portfolio optimization presents a considerable challenge. Traditional methods often fail to keep pace with the dynamic market shifts. However, machine learning algorithms are emerging as a innovative solution to enhance copyright portfolio performance. These algorithms process vast information sets to identif