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Traffic flow prediction is the core of Intelligent Transportation Systems (ITS), with spatiotemporal characteristics being the most important feature. Due to the complex spatial correlations and time dependencies between different roads, traffic flow prediction has become a challenging task. Currently, prediction methods based on graph convolutional neural networks still have room for optimization in terms of feature perception and extraction at both local and global levels of the network. To address abpve issues, this paper proposes an optimized model based on graph neural networks: the Diffusion Mutual Convolutional Recurrent Neural Network (DMCRNN). This model is based on the DCRNN benchmark model and utilizes a mutual learning strategy to optimize it. During training, two DCRNN networks mutually learn from and guide each other to enhance their respective feature learning capabilities. The effectiveness of the optimization strategy is verified on two real datasets, METR-LA and PEMS-BAY. The results show that the optimized model significantly reduces prediction errors, with a decrease in MAE of 0.15 and 0.12 for one hour on the two datasets respectively, indicating that the mutual learning optimization strategy has good performance.