Research has shown that seismic waves usually undergo different degrees of velocity dispersion and attenuation when they encounter hydrocarbon-bearing reservoirs during propagation, which also leads to a close correlation between the reflection coefficient and frequency. Therefore, we can utilize the velocity dispersion property extracted by hydrocarbon-bearing reservoirs AVO inversion for fluid identification. Frequency-dependent AVO inversion is performed based on the amplitude spectrum obtained from the time-frequency analysis of seismic data. The resolution and accuracy of the time-frequency analysis are critical factors influencing the results of dispersion attribute inversion. In recent years, time-frequency analysis methods based on sparse representation have gained attention due to their high time-frequency resolution. This paper proposes a more flexible sparse time-frequency analysis method based on compressed sensing theory and constrained by the L_P quasi-norm. Numerical models demonstrate that this method achieves higher resolution time-frequency distributions, making it suitable for seismic signal analysis. By integrating this L_P quasi-norm sparse time-frequency analysis method with frequency-dependent AVO inversion, it is possible to accurately extract P-wave dispersion attributes, thereby identifying fluids in reservoirs. Field data validation shows that the frequency-dependent AVO inversion method based on sparse time-frequency analysis not only provides high resolution but also offers reliable fluid indicators for hydrocarbon reservoirs, offering strong technical support for the identification of complex reservoirs.