In the research of bird sound recognition, the selection of sound features has a great impact on the accuracy of recognition and classification. To improve the accuracy of bird sound recognition, this study starts with the problem that the traditional Mel frequency cepstral coefficient (MFCC) characterizes the high-frequency information in bird sound insufficiently. Feature fusion of MFCC based on Fisher criterion and inverted MFCC (IMFCC) is proposed to obtain a new feature parameter MFCC-IMFCC that can be applied to bird sound recognition to improve the characterization of the high-frequency information in bird sound. Meanwhile, the penalty factor C and the kernel parameter g in the support vector machine (SVM) are optimized by a genetic algorithm (GA), and a GA-SVM classification model is trained. Experiments show that under the same conditions, the recognition rate of the MFCC-IMFCC approach is higher than that of the MFCC one.