Aiming at the problems of single classification mode and poor universality of existing epilepsy EEG recognition algorithms, a novel EEG signal automatic recognition method is proposed based on DoubleDensity Discrete Wavelet Transform (DDDWT) and LogLogistics parameter regression (LLPR). This method not only utilizes the decomposition capacity of DDDWT algorithm, but also constructs the LLPR model for EEG signal, integrates the two algorithms organically, and fully exploits the advantages of the two algorithms. In this study, the filtered EEG signals are decomposed into six levels with DDDWT, and the wavelet coefficients of various subbands are transformed to the energy waveforms in wavelet domain to acquire the feature parameters using the LLPR model. The scale parameter α and shape parameter β are calculated to characterize the EEG signal. The feature parameters extracted from all the subbands are composed as the eigenvalues, which are fed to support vector machine (SVM) optimized with genetic algorithm (GA) to obtain the final classification results, thus the EEG signal automatic recognition is achieved. When the proposed method was used to deal with two multimode EEG classification problems of A＼D＼E and AB＼CD＼E, the satisfied accuracies of 98.9% and 97.75% were obtained respectively. Experiment results indicate that the proposed method can meet the actual application requirement, is more appropriate for solving the recognition problems of multiclass EEG signals, has good universality and classification performance, and has great value in practical applications dealing with epileptics.