A new path planning algorithm was proposed to address the limitations of the conventional rapidly-exploring random trees (RRT) path planning algorithm, including excessive redundant sampling points, increased randomness, and lack of smooth paths. A model named the optimal path area prediction network (OPAPN) was developed to predict potential optimal path areas within the map using deep learning techniques. A global feature extraction module, a hybrid attention mechanism, and switchable atrous convolution techniques were incorporated in the model. The network’s understanding of the map’s overall layout and start/goal information was enhanced by the components to reduce unnecessary computational burdens. The number of sampling points was reduced significantly through heuristic sampling in the optimal path regions predicted by OPAPN, and the algorithm’s convergence speed was accelerated via a dual-tree expansion strategy. Both simulation experiments and real-world tests showed that the proposed algorithm performed well in convergence time, node count, and path length, confirming its practical application value.