Establishing an accurate rolling bearing performance degradation prediction model plays a crucial role in subsequent processing such as bearing fault classification and life prediction. In order to solve the problem of inaccurate prediction of bearing performance degradation model, an improved bat algorithm (IBA) is proposed to improve the accuracy of degradation model prediction. Firstly, Cat chaotic mapping is applied to the initial position of the population to enhance the traversability of the population and improve the quality of the initial solution; secondly, an inverse tangent-like control factor is added in the iterative process to improve the algorithm"s accuracy in finding the optimum; finally, the position updating strategy is improved to prevent from falling into the local optimum. By comparing the results with those obtained from the support vector regression machine (SVR) optimized by Bat Algorithm (BA), SVR optimized by Particle Swarm Optimization Algorithm, and SVR optimized by Gray Wolf Optimization Algorithm, the results show that the absolute mean error of the prediction model optimized by the IBA decreases by 70.60%, 67.19%, 55.56%, and the root-mean-square error decreases by 76.64%, 76.12%, and 76.12%, respectively. 76.64%, 76.12%, and 30.29%, respectively, further proving the accuracy of the improved prediction model.