As the uncertainties of intermittent energy and load in the integrated energy system gradually increase, traditional dispatch methods are limited to fixed physical models and parameter settings that can hardly respond to the random fluctuations in the dynamic system with source-load. In this paper, a deep reinforcement learning-based dynamic dispatch method for the integrated energy system is proposed to address this problem. First, a data-driven deep reinforcement learning model is constructed for the integrated energy system. Through the continuous interaction between agent and integrated energy system, the dispatch strategies are learned adaptively to reduce dependence on the physical models. Secondly, the variations of source-load uncertainties are characterized by adding random disturbances. Pivotal aspects such as state spaces, action spaces, reward mechanisms and the training process of the deep reinforcement learning model are improved according to the characteristics of uncertainties. Then a proximal policy optimization algorithm is used to solve the problem, and the dynamic dispatch decisions of the integrated energy system are realized. Finally, simulation results verify the feasibility and effectiveness of the proposed method over different time scales and in uncertain environments. This work is supported by the National Natural Science Foundation of China (No. 51977005).