The invention discloses a method for detecting a P300 electroencephalogram based on a convolutional neural network, which is used for a brain-computer interface classification algorithm and is capable of effectively solving a small sample problem in the conventional classification algorithm while improving the classification accuracy. Through using a thought of an image recognition field for reference, the method fully utilizes thoughts of a local receptive field and weight sharing of the convolutional neural network to take a typical P300 electroencephalogram acquisition sample as an analogy of a feature image, the sample characteristics are extracted through a continuous convolution process, and through carrying out feature mapping on a down sampling process, feature extraction and feature mapping are continuously performed, so that the sample characteristics are more simplified, meanwhile, through applying the local receptive field and weight sharing, network weighting parameters and computation complexity are greatly reduced to facilitate popularization of the algorithm. The experimental result shows that through the method adopted in the invention, the classification accuracy is effectively improved, the system stability is increased, and the method has better application prospect.