Abstract: Aiming at the problem of accuracy and rapidity of audio event detection in the mass-data audio processing tasks, a generic method of rapidly recognizing audio event based on 2D-Haar acoustic super feature vector and AdaBoost is proposed. Firstly, it combines certain number of continuous audio frames to be an "acoustic feature image", secondly, uses AdaBoost.MH or fast Random AdaBoost feature selection algorithm to select high representative 2D-Haar pattern combinations to construct super feature vectors; thirdly, analyzes the commonality and differences between subcategories, then extracts common features and reduces different features to obtain a generic audio event template, which can support the accurate identification of multiple sub-classes and detect and locate the specific audio event from the audio stream accurately. Experimental results show that the use of 2D-Haar acoustic feature super vector can make recognition accuracy 5% higher than ones that MFCC, PLP, LPCC and other traditional acoustic features yielded, and can make the training processing 7~20 times faster and the recognition processing 5~10 times faster, it can even achieve an average precision of 93.38%, an average recall of 95.03% under the optimal parameter configuration found by grid method. Above all, it can provide an accurate and fast mass-data processing method for audio event detection.