Abstract: Birdsong is one of the most important features of bird biology. The selection of bird song characteristic parameters and the improvement of birdsong classification accuracy have been the research directions of scholars. Based on birdsong recognition technology, a kernel extreme learning machine classification model based on krill herd optimization was proposed. The mel frequency cepstral coefficient (MFCC) was used to extract the features of the representative 30 kinds of bird sound signals around Shanghai. The extracted feature parameters were identified and classified by extreme learning machine (ELM) as the basic classification model. The basic model was optimized with the combination of the kernel function idea. The krill herd algorithm (KHA) algorithm was used to optimize the training parameters for the realization of the recognition and classification of bird song signals. In order to verify the classification performance and stability of the krill herd-optimized kernel extreme learning machine (KEML) classification model, 5, 10, 20 and 30 bird sound signals were classified and compared with the ELM, BP, SVM and KELM classification models, as well as the KELM model based on genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). The results showed that the classification recognition rates of the krill herd optimization kernel extreme learning machine classification model were 99.65%, 97.79%, 94.48% and 89.21%, respectively, with higher classification accuracy, stability and stronger generalization ability.