Abstract: Physics-informed neural network algorithms provide an effective solution for surrogate solving of partial differential equations by integrating deep learning with physical laws. Because of the low convergence efficiency and prediction accuracy of clasical physics-informed neural network caused by spectral bias, a physics-informed neural network algorithm (EM-RBN) based on integrated networks and dynamic weight generation was proposed. This algorithm captures cross-frequency features by designing a multi-layer perceptron based on data augmentation transformation and a radial basis sub network. Additionally, a dynamic weight generation module is introduced to learn and adjust the output weights of sub-networks in real time, thereby balancing the contributions of high and low frequency features. Experimental results show that the EM-RBN method achieves a maximum prediction accuracy improvement of two orders of magnitude compared with existing physics-informed neural network algorithms across different numerical problems. The code of this paper is open-sourced at https: //github.com/jia-chen25/EM-RBN.