In order to address the security issues that may arise in the sequential decision-making process of deep reinforcement learning, this paper studies a motion planning method based on multi-agent event triggered hierarchical security reinforcement learning(MEHSRL) method. Firstly, this method constructs a multi-agent twin delayed deep deterministic policy gradient algorithm based on the constrained Markov decision model. The model uses state security events as trigger conditions to implement hierarchical reinforcement learning in different state spaces. Then, by introducing a Lyapunov evaluation network, additional safety constraint rules are constructed for the reinforcement learning network, and the safety of robot decision is ensured by multi constraint objective optimization learning. Finally, the proposed method is tested in the security reinforcement learning scenario. The results show that proposed method achieves the goal of restoring the state trajectory from the dangerous state to the safe space in a limited time, improving the security of the strategy, and the effect of motion planning is better than the comparison method.