For transporting large cargoes in practice, the triple pendulum hoisting pattern is widely utilized, where the distributed mass payload (DMP) is suspended under the hanger, the hanger is suspended on the hook, and the hook is connected with the hoisting mechanism. Compared with single pendulum and double pendulum cranes, triple pendulum cranes, which control 4 degrees of freedom (DoFs) with merely 1 control input, have a higher underactuation degree, with higher control difficulties. To solve the above problems, a model-free deep reinforcement learning-based trajectory planning method is proposed for triple pendulum DMP cranes. Finally, experiments are carried out on a self-built 2-ton crane experimental platform. Hardware experimental results demonstrate that the DMP residual swing is effectively suppressed.