  This paper proposes an integrated approach combining computer networks and
artificial neural networks to construct an intelligent network operator,
functioning as an AI model. State information from computer networks is
transformed into embedded vectors, enabling the operator to efficiently
recognize different pieces of information and accurately output appropriate
operations for the computer network at each step. The operator has undergone
comprehensive testing, achieving a 100% accuracy rate, thus eliminating
operational risks. Furthermore, a novel algorithm is proposed to emphasize
crucial training losses, aiming to enhance the efficiency of operator training.
Additionally, a simple computer network simulator is created and encapsulated
into training and testing environment components, enabling automation of the
data collection, training, and testing processes. This abstract outlines the
core contributions of the paper while highlighting the innovative methodology
employed in the development and validation of the AI-based network operator.
