A window-sliding based narrowest significance pursuit (WNSP) algorithm is proposed for multiple change-points estimation. The algorithm adopts a "post-inference selection" approach: first, it automatically identifies the narrowest significant intervals containing at least one change point using the narrow significance tracking (NSP) method at a global significance level $\alpha$; then, within each interval, it employs adaptive bandwidth and single-peak detection techniques to achieve precise estimation of change-point locations. Theoretical analysis confirms the method's consistency and finite-sample reliability under general noise conditions. Numerical simulations and real-world data analysis demonstrate the WNSP algorithm's effectiveness and robustness across diverse noise distributions and signal structures.