Correctly identifying and classifying pulse signals of whales and active sonar or communication signals are very important for improving the stability and reliability of the marine passive acoustic monitoring and underwater sonar or underwater acoustic communication systems. In this paper, the representative Click signals of whales and three kinds of traditional sonar signals are selected as research objects, and a method for classifying sperm whale clicks and traditional sonar signals based on time-frequency features is proposed. Firstly, the denoising and automatic signal extraction of whale clicks are realized by using filtering, wavelet denoising and endpoint detection methods. Then, based on the short-time Fourier transform of the four types of signals, polynomials are used to fit the signal time-frequency contours, and the coefficients of the fitted polynomial are extracted as the time-frequency features of signals. Finally, the four types of signals are classified and identified using back propagation (BP) neural network and support vector machine respectively. The classification results verify the effectiveness of the proposed algorithm and method.