Industrial parks in China are significant contributors to the country’s carbon dioxide emissions. Prioritizing the achievement of carbon neutrality in parks is a crucial in helping China reach its ‘dual-carbon’ goal. This paper presents the construction of a low-carbon park integrated energy system. The system incorporates electrolyzers and hydrogen-blended gas turbines with carbon capture technology into the energy supply side, and considers various flexible resources on the storage, supply, and consumption sides. To efficiently optimize the low-carbon economic dispatch of various flexible resources in this integrated energy system, a TD3 reinforcement learning algorithm considering behavioral cloning is proposed for offline training and online optimization. Finally, the superiority of the proposed optimization strategy is verified through simulation examples.