To reduce the blocking rate of multi-robot path planning in dynamic environments， a Distributed Communication and local Attention based Multi-Agent Path Finding （DCAMAPF） was proposed based on Actor-Critic deep reinforcement learning method framework， using request-response communication mechanism and local attention mechanism. In the Actor network， local observation and action information was requested by each robot from other robots in its field of view based on the request-response communication mechanism， and a coordinated action strategy was planned accordingly. In the Critic network， attention weights were dynamically allocated by each robot to the local observation and action information of other robots that had successfully responded within its field of view based on the local attention mechanism. The experimental results showed that， the blocking rate was reduced by approximately 6.91， 4.97， and 3.56 percentage points， respectively， in a discrete initialization environment， compared with traditional dynamic path planning methods such as D^* Lite， the latest distributed reinforcement learning method MAPPER， and the latest centralized reinforcement learning method AB-MAPPER （Attention and BicNet based MAPPER）； in a centralized initialization environment， the mean blocking rate was reduced by approximately 15.86， 11.71 and 5.54 percentage points； while the occupied computing cache was also reduced. Therefore， the proposed method ensures the efficiency of path planning and is applicable for solving multi-robot path planning tasks in different dynamic environments.