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To make the modulation classification system more suitable for signals in a wide range of signal to noise ratio (SNR), a feature extraction method based on signal wavelet packet transform modulus maxima matrix (WPTMMM) and a novel Support Vector Machine Fuzzy Network (SVMFN) classifier is presented in this paper. The WPTMMM feature extraction method has less computational complexity, more stability and has the outstanding advantage of robust with the time and white noise. Further, the SVMFN employs a new definition of fuzzy density which incorporates accuracy and uncertainty of the classifiers to improve recognition reliability to classify nine digital modulation types (i.e. 2ASK, 2FSK, 2PSK, 4ASK, 4FSK, 4PSK, 16QAM, MSK and OQPSK). Computer simulation shows that the proposed scheme has the advantages of high accuracy and reliability (success rates are over 98% when SNR is not lower than 0dB), and adapt to engineering applications.