Harris Hawk algorithm has problems such as easy precocious puberty, falling into local optimal traps, and poor stability. In order to improve the performance of the algorithm, this paper proposes an improved Harris Hawk algorithm using deep deterministic policy gradient (DDPG).DDPGHHO combines deep reinforcement learning with heuristic algorithm, trains neural network by using deep deterministic policy gradient, dynamically generates key parameters of HHO through neural network, balances global search and local search, and endows the algorithm with the ability to jump out of local optimal traps in the later period. Through the comparative experiments of function optimization and path planning, the results show that the DDPGHHO has certain generalization and excellent stability, and can search the better path in different environments.