Coarse-Grained Reconfigurable Arrays (CGRAs) has gradually become a research hotspot to satisfy the growing demand for computing power and efficiency. However, the execution efficiency of CGRA depends on the mapping framework. Traditional mapping algorithms relying on combinational logic or heuristics struggle with growing application complexity due to high compilation costs and poor mapping effectiveness, as they lack the ability to learn from experience. Reinforcement Learning (RL) has been increasingly adopted for mapping strategies, but long-dependency routings often become bottlenecks, limiting RL-based algorithms. To this end, this paper proposes NFMap, a mapping algorithm that incorporates a network flow-based algorithm to optimize the DFGs through node fusion. NFMap consists of three steps: First, NFMap adpots the network flow-based algorithm with the resource constraints of the hardware platform, and the nodes are fused into node blocks. Then, NFMap generates attributes for the node blocks for RL mapping. Finally, NFMap uses the reinforcement learning-based algorithm for end-to-end mapping. Experiments show that compared to state-of-the-art RL-based mapping framework E2EMap, NFMap achieves an average mapping quality and compilation speedup of \(1.43\times \) and \(1.14\times \) , respectively.