在使用传统方法处理稳态视诱发电位（steady-state visual evoked potentials, SSVEP）的脑电信号时，特征提取的准确性和充分性存在不足，影响信号的识别准确率。为此提出了一种基于卷积神经网路（convolutional neural networks, CNN）与卷积注意力机制模块（convolutional block attention module, CBAM）和长短时记忆网络（long short-term memory, LSTM）的信号分类识别方法。以CNN为基础框架，通过引入注意力机制对通道及空间特征进行充分提取，加入LSTM提高对时序特征的提取能力，实现对SSVEP信号的目标识别。实验结果显示，所提方法能够充分有效的提取各级特征且识别准确率较高，相比于典型相关分析方法（canonical correlation analysis, CCA）、CNN、CBAM-LSTM、CNN-CBAM识别准确率分别提高了5．3%、2．95%、2.27%、1．71%，可见该模型对SSVEP信号的分类识别有较好的效果。

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.