A new algorithm based on optimized match pursuit (MP) sparse decomposition using adaptive particle swarm optimization (PSO) is proposed to address the recognition problem of sound events in public environment. Based on MP sparse decomposition analysis, the fitness function was used to improve the adaptive setting of parameters related to PSO algorithm; then, the objective function and signal reconstruction function were constructed for optimizing sparse decomposition, thus realizing adaptive PSO algorithm optimized MP sparse decomposition. Moreover, the continuous Gabor super complete set was used to improve the matching degree of the optimal atom, which enhanced the sound signal and improved the classification performance of the feature. Finally, optimized support vector machine (SVM) and composite features were used to achieve accurate recognition of sound events in public environments. Experimental results show that the proposed algorithm significantly reduces computational complexity, achieves optimal recognition rate, and demonstrates better robustness compared with existing algorithms.