In power systems, frequency control is essential to ensure stable grid operation. With the increasing share of renewable energy, frequency fluctuations have become more complex and unpredictable. To address this challenge, the paper proposes a cooperative control method for power grid frequency based on the Expert-Guided Deep Deterministic Policy Gradient (EGDDPG) algorithm. This method is data-driven and continuously interacts with the environment to adaptively optimize the control strategy. On the one hand, EGDDPG incorporates expert data during training to guide the agent, enabling faster learning of effective strategies and reducing training convergence time. On the other hand, random disturbances are introduced to simulate the uncertainties in the power system, encouraging the agent to learn control strategies across different environmental states. This approach avoids the overfitting issues associated with fixed disturbance training, enhancing the adaptability and robustness of the algorithm. Simulation results show that the proposed EGDDPG algorithm converges faster and demonstrates stronger control capability and adaptability across various scenarios. It effectively reduces frequency deviation fluctuations and overshoot.