The invention discloses an ECG signal classifying method based on a wavelet packet and approximate entropy. The method comprises the steps that wavelet packet decomposition is conducted on preprocessed ECG signals, the wavelet packet coefficients of all nodes are extracted, and then the approximate entropy of all the wavelet packet coefficients is calculated; the obtained approximate entropy is used as a feature vector to be input into a support vector machine classifier, a particle swarm algorithm is used for seeking the optimal parameter of the support vector machine classifier, and various ECG signals are classified. Wavelet packet decomposition is an effective method for analyzing non-stationary signals. The approximate entropy can be used for obtaining stable estimation values only by a small amount of data, good noise-proof and anti-interference capacity is achieved, and the approximate entropy can both be used no mater what the signals are, stochastic or deterministic. According to the ECG signal classifying method, an algorithm for extracting feature vectors is simple, dimensionality reduction in a traditional method is of no need, the speed is high, consumed time is small, the classifying accuracy is high, and the ECG signal classifying method is suitable for an ECG automatic analysis auxiliary diagnosis system.