Aiming at the problem of insufficient expansion ability and low recognition rate in radar emitter recognition, an intelligent recognition algorithm based on the deep learning of time-frequency feature was proposed. The shallow two-dimensional time-frequency features with high recognition and stability were quickly extracted by down sampling of short-time Fourier transform, and the noise reduction and other pre-processing were completed by using the sparseness of the local frequency-domain signal; a convolutional neural network for deep feature learning and recognition was designed, and the scale of the network was expanded by different scale convolution kernels to enhance the feature representation ability; the network was trained and tuned by using eight kinds of emitter signals under high SNR(signal-to-noise ratio) conditions, and the effectiveness of the algorithm and network was verified by a low SNR sample. The experimental results showed that the system achieves overall recognition rate of 98.31% at SNR of -8 dB, which verifies that the proposed algorithm has strong robustness.