When traditional methods were used to evoked the potentials SSVEP (steady-state visual evoked potentials) EEG(electroencephalogram) signals, the accuracy and sufficiency of feature extraction were insufficient, which affected the recognition accuracy of signals. A novel approach was proposed which based on a CNN (convolutional neural network) integrated with a CBAM(convolutional block attention module) and a LSTM (long short-term memory network). By incorporating attention mechanisms, both channel and spatial features were effectively extracted within the CNN framework. Additionally, LSTM was introduced to enhance the extraction of temporal features, enabling accurate recognition of SSVEP signals. The experimental results show that the proposed method can effectively extract hierarchical features and achieves a high recognition accuracy. Compared to canonical correlation analysis(CCA), CNN, CBAM-LSTM, and CNN-CBAM, the proposed model improves the recognition accuracy by 5.3%, 2.95%, 2.27%, and 1.71% respectively. It can be seen that the model has a good performance in the classification and recognition of SSVEP signals.