In order to improve the detection accuracy of P300 EEG signals in non-invasive brain-computer interface (BCI) system, this paper proposes a CNN-LSTM combined network model based on convolutional neural network (CNN) and long short-term memory (LSTM) network. The convolutional network adopts a hierarchical structure, and designs a one-dimensional convolution kernel that matches different feature dimensions; long short-term memory network (LSTM) is used to explore the interdependence of data time series, learning Correlation of global features for object classification. The test results show that the model proposed in this paper has a detection accuracy of 91.28% for the single-trial P300 signal induced by the experiment. Compared with the EEGNet network and the support vector machine(SVM) algorithm, the accuracy is increased by 2.18% and 8.31%, respectively. It also achieves the optimal performance under the evaluation indicators of Precision, Recall, F1 score and AUC value, and has strong generalization performance.