Currently, the brain-machine interface based on steady-state visual evoked potential (SSVEP) has received wide attention in human-computer collaboration, and the existing deep learning classification methods oriented to the phase and frequency information of SSVEP signals still have problems such as poor classification of SSVEP signals due to insufficient utilization of the information. And a variety of classification algorithms have appeared for solving the above problems. In this paper, a deep neural network model for SSVEP signal classification is proposed based on the idea of migration learning, which takes the complex vectors after the fast Fourier transform as inputs, and convolves the real and imaginary part vectors of each lead to learn the corresponding phase frequency characteristics. The model is divided into two parts: the first part uses the statistical commonality among all subjects to obtain the global phase-frequency feature module for phase and frequency information; the second part uses the trained global phase-frequency feature module to initialize the local phase-frequency feature module, and fine-tunes the training parameters through further reinforcement learning of the local phase-frequency feature module in order to reduce the individual differences between each subject. Tested on the public dataset BETA, the average accuracy and average information transfer rate are 89.98% and 71.80 bit/min, respectively, when the time window length is 1.5 s. The experimental results show that the classification algorithm model in this paper achieves a relatively good classification effect compared with other methods, and the designed global and local phase-frequency feature modules are able to improve the effect of individual differences on the classification results. The designed global and local phase-frequency feature module can improve the influence of individual differences on the classification results, which provides a brand new idea for the in-depth mining and utilization of phase and frequency information in SSVEP signals.