In the volatile sphere of copyright, portfolio optimization presents a substantial challenge. Traditional methods often fail to keep pace with the dynamic market shifts. However, machine learning algorithms are emerging as a powerful solution to enhance copyright portfolio performance. These algorithms analyze vast datasets to identify patterns and