Integrated energy systems enable the supply of multiple forms of energy, but at the same time emit a large amount of carbon dioxide, which also affects the surrounding environment. An optimal scheduling strategy based on double delay depth deterministic strategy gradient algorithm is proposed for low carbon economy scheduling in integrated energy systems. Firstly, taking the minimum operation cost as the objective function, a comprehensive energy system model with multiple complementary energies of electricity, heat and cold is established considering carbon capture technology and power to gas technology. Secondly, introduce a carbon trading mechanism to improve the enthusiasm of energy conservation and emission reduction in optimal scheduling. Then, according to the reinforcement learning framework, the state space, action space and reward function of the optimization model are designed, and the agents in the TD3 algorithm are used to interact with the environment to explore strategies and learn the operation strategy of the integrated energy system. Finally, we use the historical data to train the agent of TD3 algorithm, and compare the linear programming algorithm and particle swarm optimization algorithm in different scenarios. The results show that the proposed method can reduce the carbon emission and operating cost of the integrated energy system, and can realize the low-carbon economic dispatch of the integrated energy system.