We propose a hybrid algorithm that combines cross-correlation with optical flow pyramid to address the lack of accuracy and insufficient adaptability in particle image velocimetry (PIV) hybrid algorithms. The traditional cross-correlation response is optimized to enhance the signal strength, while the optical flow pyramid structure, integrated with image warping operations, improves the initial flow estimation accuracy. The combination of these two methods greatly enhances the overall performance of the hybrid algorithm in complex flow fields. A simulation experiment is conducted to reconstruct the direct numerical simulation turbulent velocity fields, revealing that the root mean square error (RMSE) and average angle error (AAE) of the proposed hybrid algorithm are reduced by at least 33.68% and 47.22%, respectively, compared to traditional algorithms. Furthermore, compared to previous hybrid algorithm Hybrid-Liu, the RMSE and AAE are reduced by 24.93% and 33.99%, respectively, while the efficiency is improved by 20.10%. Additionally, a simulated analysis of a double-vortex structure is performed to assess the impact of different particle sizes and displacement conditions on the accuracy and adaptability to fluid scenarios of the proposed algorithm. Furthermore, the experimental measurements are used to analyze real flow fields, demonstrating that the proposed hybrid algorithm can produce velocity field results that are consistent with those obtained using common algorithms, while improving the extraction of vortex structures.