The sliding window kernel recursive least squares (SW-KRLS) algorithm has been widely used in system identification because of its simple structure, low computational complexity, and high predictive accuracy. However, with the increase of input data, high computational complexity will worsen the algorithm performance, and there are some difficulties in adapting to the system with an abrupt change. In view of this, we propose a variable sliding window sparse kernel recursive least squares (VSWS-KRLS) algorithm. In order to obtain a parsimonious kernel matrix with satisfactory prediction accuracy, the basic pruning technique is applied to the traditional sliding window method. In addition, the mechanism for window size adjustment is added to adjust the size of sliding window adaptively according to the system changes. Finally, Novelty criterion (NC), dictionary with sliding window, variable sliding window technique, and mutation detection mechanism are combined with KRLS to form our improved KRLS algorithm. The improved algorithm can reduce the computational complexity, improve the convergence performance, and have the capability to better track the system with an abrupt change. System identification experiments are carried out in the wiener nonlinear system to prove the effectiveness of the improved algorithm.