Abstract: In order to identify birds in the wild through low-cost embedded systems, a bird sound recognition method based on feature fusion and B-SVM is proposed. The original feature parameters are composed of Mel frequency cepstrum coefficient(MFCC), inverted Mel frequency cepstrum coefficient, short-time energy and short-time zerocrossing rate extracted from birdsong signal, and the original feature parameters are fused by linear discriminant algorithm. By using the black widow algorithm to optimize the kernel parameters and loss values of the support vector machine model through a test set, the B-SVM model is obtained. In the Xeno-canto birdsong dataset, the recognition accuracy of this method is 93.23%. The size of the dimension parameters of the linear discriminant algorithm and the level of the fused feature dimension are important factors that affect the recognition performance of the algorithm.Under the same conditions, the bird sound recognition algorithm developed in this paper based on feature fusion and B-SVM model has a higher recognition accuracy compared to other feature parameters and models. It provides a reference for wild bird recognition.