To solve the problem of low recognition rate of motor imagery ElectroEncephaloGram (EEG) signals, considering that EEG signals contain abundant time-frequency information, a recognition method based on Convolutional Neural Network (CNN) in time-frequency domain was proposed. Firstly, Short-Time Fourier Transform (STFT) was applied to preprocess the relevant frequency bands of EEG signals to construct a two-dimensional time-frequency domain map composed of multiple time-frequency maps of electrodes, which was regarded as the input of the CNN. Secondly, focusing on the time-frequency characteristic of two-dimensional time-frequency domain map, a novel CNN structure was designed by one-dimensional convolution method. Finally, the features extracted by CNN were classified by Support Vector Machine (SVM). Experimental results based on BCI dataset show that the average recognition rate of the proposed method is 86.5%, which is higher than that of traditional motor imagery EEG signal recognition method, and the proposed method has been applied to the intelligent wheelchair, which proves its effectiveness.