Aiming at the problems of motor bearings being prone to failure, the traditional fault diagnosis method has long time, low diagnostic accuracy and many adjustment parameters, and this paper proposes a bearing fault diagnosis method for support vector machine SVM optimized by improving sparrow algorithm ISSA. The classification algorithm introduces improved Tent chaos mapping, flock algorithm random following strategy, adaptive t distribution and dynamic selection strategy in the traditional sparrow optimization algorithm, and first uses CEEMDANenergy entropy to decompose the vibration signal, selects the energy entropy values of the five IMF components with the greatest correlation with the original signal as the eigenvector, and then inputs it to the ISSASVM classifier for bearing fault diagnosis. Experimental comparison with PSOSVM、GWOSVM and SSASVM classification models shows that the diagnostic accuracy of the ISSASVM diagnostic model can reach up to 100%.