This paper presents a novel intelligent method based on local mean decomposition and multi-class reproducing wavelet support vector machines (RWSVMs), which are applied to detect leakage in natural gas pipelines. First, local mean decomposition is used to construct product function components to decompose the leakage signals. Then, we select the leakage signals which contain the most leakage information, according to the kurtosis features of these signals, through principal component analysis. Next, we reconstruct the principal product function components in order to acquire the envelope spectrum. Finally, we confirm the leak aperture by inputting envelope spectrum entropy features, as feature vectors, into the RWSVMs. Through analysing the pipeline leakage signals, the experiments show that this method can effectively identify different leak categories.