Concerning the influence of various environmental ambiances on sound event recognition, a sound event recognition method based on Optimized Orthogonal Matching Pursuit (OOMP) and Deep Belief Network (DBN) was proposed. Firstly, Particle Swarm Optimization (PSO) algorithm was used to optimize Orthogonal Matching Pursuit (OMP) sparse decomposition of sound signal, which realized fast sparse decomposition of OMP and reserved the main body of sound signal and reduced the influence of noise. Then, an optimized composited feature was composed by Mel-Frequency Cepstral Coefficient (MFCC), time-frequency OMP feature and Pitch feature extracted from the reconstructed sound signal, which was called OOMP feature. Finally, the DBN was employed to learn the OOMP feature and recognize 40 classes of sound events in different environments and Signal-to-Noise Ratio (SNR). The experimental results show that the proposed method which combined OOMP and BDN is suitable for sound event recognition in various environments, and can effectively recognize sound events in various environments; it can still maitain an average accuracy rate of 60% even when the SNR is 0 dB.